JP6710103B2 - Parameter identification device, motor control system, parameter identification method and program - Google Patents

Description

The present invention relates to a parameter identification device, a motor control system, a parameter identification method, and a program.

In order to improve control performance in designing a motor control system (motor control system) that drives a load, there is a limit in feedback control, and it is necessary to perform feedforward control with high response characteristics to a command signal. Further, in order to realize the feedforward control, the control system side needs to have an inverse model of a model imitating a controlled object (that is, a mechanical system having a motor and a load). If there is an error between the actual characteristics of the controlled object and the control performance cannot be improved.

Therefore, in order to realize feedforward control with high control performance, it is necessary to obtain a model that accurately mimics the controlled object. Here, the inertia moment J, the viscosity coefficient D, and the Coulomb friction coefficient τ _f of the entire controlled object, which are model parameters forming the model, are calculated by a predetermined error system when the entire controlled object is modeled as a one-inertia system model. It is possible to identify the error signal based on the error signal and its internal signal.

Further, in Patent Document 1, as a model parameter estimation means, the magnitude of stiffness and friction at saturation is estimated from the phase diagram of displacement-deformation amount (lost motion correction model), and the lost motion correction amount is calculated. A method is disclosed.
In addition, Patent Document 2 discloses a method in which each model parameter is obtained from the measurement result of the relationship between force and displacement by measurement at the time of attachment and measurement by a force measuring device and used for the timing of lost motion correction.

JP-A-8-152910 JP, 2002-023852, A

An actual control target (mechanical system having a motor and a load) may have a portion (coupling member) with low rigidity and play (dead zone) in the power transmission system from the motor to the load. For such a control target, a more accurate model can be constructed by adopting a two-inertia system model that is divided into a motor side and a load side, with a portion having the above-mentioned weak rigidity and backlash as a boundary. it can.

Here, in order to construct an accurate two-inertia system model, it is necessary to accurately identify the friction existing independently on the motor side and the load side, and the rigidity and play at the boundary.
However, in the above-described example, it is not possible to independently identify each of the friction on the motor side and the friction on the load side, and the rigidity and play characteristics in the transmission system are not considered. Therefore, it is not possible to obtain a model that accurately mimics the controlled object, and thus it is not possible to realize a control system with high control performance.

The object of the present invention is made in view of the above problems, and a parameter identification device, a motor control system, a parameter identification method, and a program capable of obtaining a model accurately simulating a mechanical system having a motor and a load. To provide.

According to one aspect of the present invention, the parameter identifying device is a parameter identifying device for identifying a model parameter of a two-inertia system model imitating a mechanical system in which a motor and a load are connected by a connecting member. A data acquisition unit that acquires a torque command value for the motor, actual measurement data of the angle and angular velocity of the motor, and actual measurement data of the angle and angular velocity of the load, the acquired torque command value, and the angle of the motor. Based on the measured data of the angular velocity and the measured data of the angle and the angular velocity of the load, a phase diagram generating unit that generates a plurality of phase diagrams, and based on the generated phase diagrams, A parameter identification unit that identifies a model parameter indicating the friction of the motor, the friction of the load, the rigidity of the connecting member, and the dead band width of the connecting member.

Further, according to an aspect of the present invention, the phase diagram generating unit generates, as the phase diagram, a torque-torsion velocity phase diagram showing a relationship between the torque command value and a torsion velocity of the connecting member. Then, the parameter identifying unit identifies a model parameter indicating the friction of the motor based on the position of the break point in the torque-twist phase diagram.

Further, according to an aspect of the present invention, the phase diagram generating unit indicates, as the phase diagram, a twist angle-twist velocity phase face indicating a relationship between a twist angle of the connecting member and a twist speed of the connecting member. A diagram is generated, and the parameter identification unit identifies a model parameter indicating the rigidity of the connecting member and the dead zone width of the connecting member based on the position of the break point in the twist angle-twist velocity phase diagram.

Further, according to an aspect of the present invention, the phase diagram generating unit, as the phase diagram, the relationship between the torque command value and at least one of the angular velocity of the motor and the angular velocity of the load. Is generated, and the parameter identifying unit identifies the model parameter indicating the friction of the load based on the position of the break point in the torque-mechanical angular velocity phase diagram.

Further, according to one aspect of the present invention, the phase diagram generating unit generates, as the phase diagram, a load angle-torque phase diagram showing a relationship between the load angle and the torque command value. The parameter identification unit identifies a model parameter indicating the friction of the load based on the position of the break point in the load angle-torque phase diagram.

Further, according to an aspect of the present invention, the phase diagram generating unit generates, as the phase diagram, a twist angle-torque phase diagram showing a relationship between a twist angle of the connecting member and the torque command value. However, the parameter identification unit, based on the position of the break point in the twist angle-torque phase diagram, the friction of the motor, the friction of the load, the rigidity of the connecting member, and the dead band width of the connecting member. A model parameter indicating at least one of them is identified.

Further, according to one aspect of the present invention, based on the actual measurement data of the torque command value, the angle of the motor, and the angle of the load, a motor-side model of the two-inertia system model with respect to the motor. A motor-side error system that outputs a motor-side error signal indicating the degree of error, and a load-side model of the two inertial system models based on the measured data of the motor angle and the load angle, A load-side error system that outputs a load-side error signal that indicates the degree of error with respect to the load; and the parameter identification unit receives the motor-side error signal and at least calculates the moment of inertia and the viscosity coefficient of the motor. The model parameter shown is identified, and the load side error signal is input to identify the model parameter showing at least the moment of inertia and viscosity coefficient of the load.

Further, according to one aspect of the present invention, the parameter identification unit, the torque command value acquired when the amplitude and frequency of the repetitive operation performed by the mechanical system is relatively small amplitude and low frequency, and Based on the measured data of the angle and angular velocity of the motor and the measured data of the angle and angular velocity of the load, the friction of the motor, the friction of the load, the rigidity of the connecting member, and the dead band width of the connecting member are determined. The model parameter shown is identified, and the torque command value acquired when the amplitude and frequency of the repetitive operation performed by the mechanical system are relatively large amplitude and high frequency, actual measurement data of the angle of the motor, and the load. On the basis of the measured data of the angle, the model parameter indicating the moment of inertia and viscosity coefficient of the motor and the model parameter indicating the moment of inertia and viscosity coefficient of the load are identified.

Further, according to one aspect of the present invention, the parameter identifying device is a parameter identifying device that identifies a model parameter of a two-inertia system model imitating a mechanical system in which a motor and a load are connected by a connecting member. , A data acquisition unit that acquires a torque command value for the motor, actually measured data of the angle of the motor, and actually measured data of the angle of the load, the acquired torque command value, and actually measured the angle of the motor. A phase diagram generating unit that generates a twist angle-torque phase diagram showing the relationship between the twist angle of the connecting member and the torque command value based on the data and the actual measurement data of the load angle, and Based on the twist angle-torque phase diagram, the friction of the motor, the friction of the load, the rigidity of the connecting member, and a parameter identifying unit for identifying a model parameter indicating the dead band width of the connecting member, Equipped with.

Further, according to one aspect of the present invention, the motor control system includes a feedback for calculating the torque command value based on the parameter identification device described above and the deviation of the current angle from the target angle of the load. A control unit and a feedforward control unit that calculates the torque command value based on the model parameter identified by the parameter identification device are provided.

Further, according to one aspect of the present invention, the parameter identification method is a parameter identification method for identifying a model parameter of a two-inertia system model simulating a mechanical system in which a motor and a load are connected by a connecting member. A step of acquiring a torque command value for the motor, actual measurement data of the angle and angular velocity of the motor, and actual measurement data of the angle and angular velocity of the load, the acquired torque command value, and the angle of the motor And a step of generating a plurality of phase diagrams based on the measured data of the angular velocity and the measured data of the angle of the load and the angular velocity, based on the generated plurality of phase diagrams, friction of the motor, Identifying model parameters indicative of friction of the load, stiffness of the connecting member, and dead band width of the connecting member.

Further, according to one aspect of the present invention, the program causes a computer of a parameter identification device that identifies a model parameter of a two-inertia system model simulating a mechanical system in which a motor and a load are connected by a connecting member, A data acquisition unit that acquires a torque command value for the motor, actual measurement data of the angle and angular velocity of the motor, and actual measurement data of the angle and angular velocity of the load, the acquired torque command value, the angle of the motor, and A phase diagram generator that generates a plurality of phase diagrams on the basis of the measured data of the angular velocity and the measured data of the angle of the load and the angular velocity, based on the generated plurality of the phase diagrams, It functions as a friction, a friction of the load, a rigidity of the connecting member, and a parameter identifying unit that identifies a model parameter indicating the dead band width of the connecting member.

According to the above-mentioned parameter identification device, motor control system, parameter identification method, and program, it is possible to obtain a model that accurately imitates a mechanical system having a motor and a load.

It is a figure which shows the outline|summary of the mechanical system which the motor control system which concerns on 1st Embodiment controls. It is a figure which shows the block diagram of the mechanical system which concerns on 1st Embodiment. It is a figure explaining the friction characteristic by the side of the motor of the mechanical system which concerns on 1st Embodiment, and a load side. It is a figure explaining the characteristic of a dead zone in the connecting member of the mechanical system concerning a 1st embodiment. It is a figure which shows the function structure of the motor control system which concerns on 1st Embodiment. It is a figure which shows the function structure of the parameter identification apparatus which concerns on 1st Embodiment. It is a figure which shows the processing flow of the parameter identification apparatus which concerns on 1st Embodiment. It is a figure explaining the process of the parameter identification apparatus which concerns on 1st Embodiment. It is a 1st figure explaining the some phase surface figure which concerns on 1st Embodiment. It is a 2nd figure explaining the some phase surface figure which concerns on 1st Embodiment. It is a 3rd figure explaining the some phase surface figure which concerns on 1st Embodiment. It is a 4th figure explaining the some phase surface figure which concerns on 1st Embodiment. It is a 5th figure explaining the some phase surface figure which concerns on 1st Embodiment. It is a 6th figure explaining the some phase surface figure which concerns on 1st Embodiment. It is a figure which shows the function structure of the parameter identification apparatus which concerns on 2nd Embodiment. It is a 1st figure explaining the process of the motor side error system and load side error system which concern on 2nd Embodiment. It is a 2nd figure explaining the process of the motor side error system and load side error system which concern on 2nd Embodiment. It is a 3rd figure explaining the process of the motor side error system and load side error system which concern on 2nd Embodiment. It is a figure which shows the processing flow of the parameter identification apparatus which concerns on 2nd Embodiment.

<First Embodiment>
Hereinafter, the motor control system according to the first embodiment will be described in detail with reference to FIGS. 1 to 14.

FIG. 1 is a diagram showing an outline of a mechanical system to be controlled by the motor control system according to the first embodiment.
As illustrated in FIG. 1, the motor control system 1 according to the first embodiment includes a motor 20, a load 21, and a shaft 22 (coupling member) that mechanically connects the motor 20 and the load 21. It is a system that controls the operation of the mechanical system 2. Here, in the present embodiment, the motor 20 is a servo motor that has a rotation detector (encoder) capable of detecting a rotation angle and a rotation speed therein and that can realize precise positioning. Further, in the present embodiment, the shaft 22 is a member having a rigid element such as a ball screw and a gear, and a play element.

As shown in FIG. 1, the motor control system 1 receives an angle command value indicating a target rotation angle (hereinafter, referred to as “target angle θ _t ”) of the load 21 from a host device (not shown). Then, the motor control system 1 calculates the torque τ that should be generated in the motor 20 according to the received target angle θ _t , and outputs the torque command value indicating the torque τ. The motor 20 is rotationally driven by the torque τ based on the received torque command value, and the torque τ is transmitted from the motor 20 side to the load 21 side through the shaft 22. As a result, the load 21 is rotationally driven according to the transmitted torque τ'. In this way, the motor control system 1 realizes control such that the rotation angle θ _L of the load 21 matches the desired target angle θ _t .

The motor control system 1 according to the present embodiment is a so-called closed system, in which actual measurement data indicating the rotational speed (angular velocity) of the motor 20 and the rotational angle of the load 21 is input, and based on these actual measurement data. Perform feedback control.
In the following description, the rotation speed (angular speed) of the motor 20 is also referred to as “motor angular speed ω _M ”, and the rotation angle of the motor 20 is also referred to as “motor angle θ _M ”. The rotation speed (angular speed) of the load 21 is also referred to as “load angular speed ω _L ”, and the rotation angle of the load 21 is also referred to as “load angle θ _L ”.
Further, the motor control system 1 further has in advance a two-inertia system model (described later) that simulates the mechanical system 2 as a two-inertia system, and performs feedforward control based on the two-inertia system model.
If the load 21 in the mechanical system 2 is not a rotary system but a linear system load (table load, etc.), the control target parameter for the load 21 is not strictly “angle” or “angular velocity” but “position”. ", "speed". However, in this case, the motor control system 1 treats the “position” and “speed” of the load as motor-axis converted values in order to handle the same as the “angle” and “angular speed” of the motor 20 as a two-inertia system. "Angle" and "angular velocity" are sequentially converted to perform various controls.

When the mechanical system 2 is regarded as a two-inertia system, the parameter group showing the characteristic peculiar to the mechanical system 2 includes the parameter showing the characteristic of the motor 20 side, the parameter showing the characteristic of the load 21 side, and the motor 20 and the load. 21 is roughly classified into parameters indicating the characteristics of the shaft 22 (coupling member) that mechanically couples with 21 to transmit power. Specifically, there are a motor-side inertia moment J _M , a motor-side viscosity coefficient D _M , and a motor-side friction coefficient τ _fM as parameters indicating the characteristics on the motor 20 side. Further, as the parameters indicating the characteristics on the load 21 side, there are a load side moment of inertia J _L , a load side viscosity coefficient D _L , and a load side friction coefficient τ _fL . Further, as a parameter showing the characteristics of the shaft 22, there is a torsional rigidity coefficient _{K R,} twist viscosity coefficient _{D R,} dead zone width BL.

(Outline of 2-inertia model)
FIG. 2 is a block diagram of a mechanical system according to the first embodiment.
By regarding the mechanical system 2 (see FIG. 1) according to the present embodiment as a two-inertia system including the motor 20, the load 21, and the shaft 22, the relationship between the input and the output in the mechanical system 2 is as shown in FIG. It can be represented by a block diagram using a transfer function.

As shown in FIG. 2, the motor 20 receives the torque τ generated based on the torque command value received from the motor control system 1 (FIG. 1 ), and outputs the motor angular velocity ω _M and the motor angle θ _M. .. The torque τ input to the motor 20 is subtracted from the friction coefficient τ _fM on the motor side, which is the friction generated in the motor 20, and the torque τ′ transmitted to the load 21 through the shaft 22, and then the transmission element 1/(J _M It is converted into a motor angular velocity ω _M through s+D _M ) and further converted into a motor angle θ _M through a transfer element 1/s.
Here, the motor side friction coefficient τ _fM is not only the Coulomb friction component in which only the sign is reversed when the speed of the motor 20 is reversed (when the sign of the motor angular speed ω _M is reversed), but also the displacement of the motor 20 after the speed reversal (motor It includes a non-linear friction component that changes non-linearly depending on the angular velocity ω _M (integral value from zero). Motor side friction coefficient tau _fM defines a modeled nonlinear friction component, obtained through the motor-side friction function G _M to an input variable motor angular velocity omega _M. Details of the motor-side friction function G _M will be described later.

Similarly, the load 21, torque _{τ '(= (K R +} D R s) (θ M -θ L -BKLS)) transmitted from the motor 20 through the shaft 22 as input, load angular velocity omega _L and load angle theta _{Let L} be the output. The torque τ′ input to the load 21 is converted into the load angular velocity ω _L via the transfer element 1/(J _L s+D _L ) after the load side friction coefficient τ _fL, which is the friction generated in the load 21, is subtracted. It is converted into the load angle θ _L through the transfer element 1/s.
Here, the load-side friction coefficient τ _fL also includes a non-linear friction component that changes non-linearly depending on the displacement of the load 21 after speed reversal (the integrated value of the load angular velocity ω _L from zero). The load-side friction coefficient τ _fL is obtained through a load-side friction function G _L in which the nonlinear friction component is modeled and defined, and the load angular velocity ω _L is an input variable. Details of the load-side friction function G _L will be described later.

A rotation detector (encoder) (not shown) is installed in each of the motor 20 and the load 21 that form the mechanical system 2. The motor control system 1 uses the rotation detector to measure the motor angle θ _M and the load angle θ _L with time, and the motor angular velocity ω _M and the load angular velocity ω _{L that} can be derived from the time derivative of these data. Acquire actual measurement data showing changes over time.

Further, as shown in FIG. 2, the shaft 22 receives a deviation (hereinafter, also referred to as “twist angle (θ _M −θ _L )”) between the motor angle θ _M and the load angle θ _L as an input, and the twist angle. This is a transmission system that outputs a torque τ′ according to (θ _M −θ _L ). Input twist angle (theta _M - [theta] _L) has a dead zone characteristic function F1 representing the nonlinearity in the dead zone (play), and transmission elements _{K R} and transmission elements _{D R} s, through, applied to the motor 20 and the load 21 Is converted to the torque τ'.
Here, the dead zone characteristic function F1 is a non-linear function that outputs the twist angle (θ _M −θ _L ) of the shaft 22 as an input variable and subtracts the backlash displacement BKLS from the twist angle (θ _M −θ _L ). Is. Details of the dead zone characteristic function F1 will be described later.
Further, the torsional rigidity coefficient K _R, a parameter indicating the degree of stiffness of the torsion axis 22, which corresponds to the spring constant of the shaft 22. That is, the torsional rigidity coefficient K _R gives a component of the torque τ′ applied to _the load 21 that is proportional to the torsional angle (θ _M −θ _L ) of the shaft 22.
Further, the twist viscosity coefficient D _R, a parameter indicating the degree of stickiness of the shaft 22, of the torque tau 'applied to the load 21, the deviation between the motor angular velocity omega _M in the shaft 22 and the load angular velocity omega _L (Hereinafter, it is also described as “twisting speed (ω _M −ω _L )”).

In the mechanical system 2 shown in FIG. 2, the motor-side inertia moment J _M , the motor-side viscosity coefficient D _M , the motor-side friction coefficient τ _fM , the load-side inertia moment J _L , the load-side viscosity coefficient D _L , and the load-side friction coefficient τ _fL , torsional rigidity coefficient K _R, twist viscosity coefficient D _R and the dead zone width BL is a parameter group representative of the actual characteristics of the mechanical system 2, both of which are unknown parameters observation is difficult to separate.
On the other hand, the torque τ which is an input to the mechanical system 2 and the motor angular velocity ω _M , the motor angle θ _M , the load angular velocity ω _L and the load angle θ _{L which} are outputs are parameters observable through the rotation detector described above. is there.

FIG. 3 is a diagram illustrating friction characteristics on the motor side and the load side of the mechanical system according to the first embodiment.
FIG. 3A is a graph showing the non-linear friction characteristics generated in each of the motor side friction coefficient τ _fM and the load side friction coefficient τ _fL .
The well-known Coulomb friction depends only on the direction (positive or negative sign) of the velocity of the object (motor angular velocity ω _M , load angular velocity ω _L ) and changes only in that direction (positive or negative symbol). It is known that the amount does not change with respect to velocity (ω) and displacement (θ). However, when the shaft 22 includes rolling elements such as a ball screw and a ball bearing, it is necessary to consider "rolling friction" having a characteristic different from that of normal Coulomb friction.
Here, the rolling friction is an apparent spring due to the displacement (integral value of the motor angular velocity ω _M and the load angular velocity ω _L from zero) after the velocity reversal in the “fine movement region” where the rolling element does not roll immediately after the velocity reversal. It has a non-linear spring characteristic in which the constant dynamically changes, and draws a hysteresis curve as shown in FIG. It is considered that this is due to elastic deformation and slippage at the contact portion between the rolling element and the raceway surface.
In the "coarse region" where the rolling elements effectively roll, Coulomb friction causes static characteristics with respect to the speed of friction. That is, as shown in FIG. 3A, the friction of the motor 20 (motor-side friction coefficient τ _fM ) is saturated with the motor-side Coulomb friction coefficient τ _fMc when the displacement after speed reversal becomes a predetermined value or more. .. Further, the friction of the load 21 (load-side friction coefficient τ _fL ) is saturated with the load-side Coulomb friction coefficient τ _fLc when the displacement after speed reversal reaches a predetermined value or more.

In the present embodiment, the motor-side friction characteristic function G _M and the load-side friction characteristic function G _L are defined based on the GMS model obtained by performing line approximation on the nonlinear friction characteristic shown in FIG. Here, the GMS model is a model in which N blocks (rolling elements) having different characteristics and a spring are connected in parallel. Each of the plurality of straight lines forming the graph (polygonal line) shown in FIG. 3B expresses how many of the N blocks have rolled (effectively rolled). ing.
As shown in FIG. 3 (b), the motor-side frictional characteristic function _{G M} is displaced _{0~θ 1, θ 1 ~θ 2,} θ 2 ~θ 3, a line to be employed in each of the theta ₃ through? ₄ slope _{K 1, K 2, K 3} , K 4 defining, and offset (intercept in displacement _{0) τ fM1 (= -τ fMc} ), τ fM2, τ fM3, τ fM4, by _τ fM5 (= τ _fMc) Stipulated.
Although not shown, the load-side friction characteristic function G _L is also defined in the same manner as the motor-side friction characteristic function G _M shown in FIG.

FIG. 4 is a diagram illustrating characteristics of a dead zone in the mechanical system connecting member according to the first embodiment.

The dead zone characteristic function F1 shown in FIG. 4A represents the transfer characteristic based on the dead zone width BL indicating the width of the "play" (dead zone) of the shaft 22 provided between the motor 20 and the load 21.
The dead zone characteristic function F1 is a non-linear characteristic that depends on the dead zone width BL, and uses the twist angle (θ _M −θ _L ) of the shaft 22 as an input variable. As shown in FIG. 4A, when the absolute value of the twist angle (θ _M −θ _L ) of the shaft 22 is equal to or less than the dead zone width BL (−BL≦θ _M −θ _L ≦+BL), the backlash output is zero. Become. That is, in this case, torque is not transmitted from the motor 20 to the load 21. On the other hand, when the absolute value of the twist angle (θ _M −θ _L ) of the shaft 22 is larger than the dead band width BL (θ _M −θ _L <−BL, θ _M −θ _L >+BL), the backlash output is the twist angle. A value smaller than (θ _M −θ _L ) by the dead band width BL (θ _M −θ _L +BL (θ _M −θ _L <−BL) or θ _M −θ _L −BL (θ _M −θ _L >+BL). ).

Further, the backlash displacement function F1′ shown in FIG. 4B has a relationship between the backlash displacement BKLS and the twist angle (θ _M −θ _L ).
The backlash displacement BKLS is a parameter indicating the amount of displacement in a dead zone having a width of the dead zone width BL (-BL to +BL), and is as shown in FIG. 4B with respect to the twist angle (θ _M −θ _L ). It has characteristics. That is, the backlash output shown in FIG. 4A can be expressed as “θ _M −θ _L −BKLS” by using the backlash displacement BKLS.
As shown in FIG. 4B, the backlash displacement BKLS has the minimum backlash BKLS (-BL) in the range where the twist angle (θ _M −θ _L ) is smaller than the minimum dead zone (−BL). In the range where the twist angle (θ _M −θ _L ) is larger than the maximum value (+BL) of the dead zone, the backlash displacement BKLS has the maximum value (+BL). Further, in the range where the twist angle (θ _M −θ _L ) is equal to or more than the minimum value (−BL) of the dead zone and equal to or less than the maximum value (+BL), the backlash displacement BKLS is the same as the twist angle (θ _M −θ _L ). It becomes a characteristic that has a value.

(Function configuration)
FIG. 5 is a diagram showing a functional configuration of the motor control system according to the first embodiment.
As shown in FIG. 5, the motor control system 1 according to the first embodiment includes a feedback control unit 10, a feedforward control unit 11, and a parameter identification device 12.

The feedback control unit 10 is a deviation (θ _t −θ) between the motor angular velocity ω _M observed through the rotation detector, the target angle θ _{t of the} load 21 specified by the angle command value, and the observed current load angle θ _{L. L} ) and the motor 20 is controlled.
Specifically, the feedback control unit 10 calculates the torque for the deviation between the target angle theta _t and the load angle theta _L a (θ _t -θ _L) to zero, the torque command value indicating the calculation result Output. At that time, the feedback control unit 10 refers to the motor angular velocity ω _M observed through the rotation detector and calculates a torque for performing appropriate and quick feedback control.

The feedforward control unit 11 internally has a two-inertia system model MOD imitating the mechanical system 2. The two-inertia system model MOD is composed of a plurality of model parameter groups and is configured to be an inverse model of the mechanical system 2 shown in FIG.
Specifically, the plurality of model parameter groups are the model motor side moment of inertia J _M0 , the model motor side viscosity coefficient D _M0 , the model motor side friction coefficient τ _fM0 , the model load side moment of inertia J _L0 , and the model load side viscosity coefficient. D _L0 , model load side friction coefficient τ _fL0 , model dead zone width BL ₀ , and model torsional rigidity coefficient K _R0 .

The two-inertia system model MOD is an inverse model constructed by imitating the mechanical system 2, and the model parameter groups (J _M0 , D _M0 , τ _fM0 , J _L0 , D _L0 , and D _L0 , which form the two-inertia model MOD. _{τ fL0, BL 0, K R0} ) , each of the unknown parameter group representative of the actual characteristics of the mechanical system _{2 (J M, D M,} τ fM, J L, D L, τ fL, BL, K R) It corresponds to.

Here, the model parameter groups _{(J M0, D M0, τ} fM0, J L0, D L0, τ fL0, BL 0, K R0) and a parameter group indicating the actual characteristics of the mechanical system 2 _{(J M, D} M _{, τ fM, J L, D} L, τ fL, BL, assuming no error at all between _{K R),} 2-inertia system model MOD is perfectly matches the actual inverse model of the mechanical system 2 Will be things. Then, the feedforward control unit 11 outputs the torque calculated based on the target angle θ _t and the two-inertia system model MOD to the mechanical system 2 to drive it, and as a result, the load angle θ _L actually obtained through the mechanical system 2 is It should _exactly match the target angle θ _t (θ _L =θ _t ). As described above, the feedforward control unit 11 calculates the torque while weaving the characteristic peculiar to the mechanical system 2 based on the two-inertia system model MOD that is defined by simulating the mechanical system 2 in advance. Also, in feedforward control with high responsiveness, more accurate control can be realized.

As shown in FIG. 5, the motor control system 1 has a torque τ (torque command value) obtained by adding the torque calculated by the feedback control unit 10 and the torque calculated by the feedforward control unit 11. ) Toward the mechanical system 2 (motor 20 (FIGS. 1 and 2)).

As described above, the motor control system 1 according to the present embodiment includes a plurality of model parameter groups (J _M0 , D) that form a two-inertia system model MOD imitating the mechanical system 2 including the motor 20, the load 21, and the shaft 22. _The torque τ to be generated in the motor 20 is calculated based on _M0 , τ _fM0 , J _L0 , D _L0 , τ _fL0 , BL ₀ , K _R0 ).

In the feedforward control unit 11, the model parameter group (J _M0 , D _M0 , τ _fM0 , J _L0 , D _L0 , τ _fL0 , BL ₀ , K _R0 ) is assumed in advance as a parameter representing the characteristic of the mechanical system 2. It is regulated by the value. However, in reality, the model parameter group (J _M0 , D _M0 , τ _fM0 , J _L0 , D _L0 , τ _fL0 , BL ₀ , K _R0 ) and the parameter group specific to the mechanical system 2 (J _M , D _M , _{τ fM, J L, D L} , τ fL, BL, between the _{K R),} an error is respectively present. If there is such an error, highly accurate feedforward control cannot be realized.
Therefore, in the motor control system 1 according to the present embodiment, among the plurality of model parameter groups described above, in particular, the model motor side friction coefficient τ _fM0 , the model load side friction coefficient τ _fL0 , the model dead zone width BL _0, and the model torsional rigidity A parameter identifying device 12 is provided that is capable of accurately identifying the coefficient K _R0 independently of each other.

As shown in FIG. 5, the parameter identification device 12 acquires actual measurement data indicating changes over time in the motor angle θ _M , the motor angular velocity ω _M , the load angle θ _L, and the load angular velocity ω _{L in} the mechanical system 2. The parameter identifying device 12 identifies the model motor-side friction coefficient τ _fM0 , the model load-side friction coefficient τ _fL0 , the model dead zone width BL _0, and the model torsional rigidity coefficient K _R0 based on the above four measured data.
Hereinafter, the function of the parameter identification device 12 will be described in detail.

FIG. 6 is a diagram showing a functional configuration of the parameter identification device according to the first embodiment.
As shown in FIG. 6, the parameter identification device 12 includes a data acquisition unit 120, a phase front view generation unit 121, and a parameter identification unit 122.

The data acquisition unit 120 measures the torque command value (torque τ) for the motor 20, the actual measurement data indicating changes over time of the motor 20 angle (motor angle θ _M ) and angular velocity (motor angular velocity ω _M ), and the angle of the load 21. (Actual load data θ _L ) and actual measurement data indicating changes in angular velocity (load angular velocity ω _L ) over time are acquired.

The phase diagram generation unit 121 is based on the torque command value (torque τ) acquired by the data acquisition unit 120, the actual measurement data of the angle and angular velocity of the motor 20, and the actual measurement data of the angle and angular velocity of the load 21. Generate multiple phase diagrams.
Here, as shown in FIG. 6, the phase diagram generator 121 indicates the “twist angle” indicating the relationship between the twist angle (θ _M −θ _L ) of the connecting member (shaft 22) and the torque command value (torque τ). A torque phase diagram P1”.
In addition, the phase diagram generator 121 indicates the “torque-mechanical angular velocity phase” indicating the relationship between the torque command value (torque τ) and the angular velocity of the motor 20 (motor angular velocity ω _M ) and the angular velocity of the load 21 (load angular velocity ω _L ). The floor plan P2" is generated.
Further, the phase diagram generator 121 indicates the relationship between the twist angle (θ _M −θ _L ) of the shaft 22, the angular velocity of the motor 20 (motor angular velocity ω _M ) and the angular velocity of the load 21 (load angular velocity ω _L ). A twist angle-mechanical angular velocity phase diagram P3" is generated.
Further, the phase diagram generator 121 is a torque command value (torque τ) and the torsional speed of the shaft 22 (the difference between the motor angular speed ω _M and the load angular speed ω _L. Hereinafter, it will be described as “ω _M −ω _L ”. ) Is generated, a "torque-torsion velocity phase diagram P4" is generated.
In addition, the phase diagram generator 121 indicates a “twist angle-twist velocity phase diagram P5” indicating the relationship between the twist angle (θ _M −θ _L ) of the shaft 22 and the twist velocity (ω _M −ω _L ) of the shaft 22. Is generated.
The phase diagram generator 121 also generates a "load angle-torque phase diagram P6" that indicates the relationship between the angle of the load 21 (load angle θ _L ) and the torque command value (torque τ).

The parameter identifying unit 122, based on the plurality of phase diagrams generated by the phase diagram generating unit 121, friction of the motor 20 (motor-side friction coefficient τ _fM ) and friction of the load 21 (load-side friction coefficient τ _fL ). , The rigidity of the shaft 22 (torsional rigidity coefficient K _R ) and the model parameters (τ _fM0 , τ _fL0 , K _R0 , B _L0 ) indicating the dead band width BL of the shaft 22 are identified.

(Processing flow of parameter identification device)
FIG. 7 is a diagram showing a processing flow of the parameter identifying device according to the first embodiment.
In addition, FIG. 8 is a diagram illustrating a process of the parameter identification device according to the first embodiment.
9 to 14 are first to sixth diagrams for explaining a plurality of phase diagrams according to the first embodiment.
The process flow shown in FIG. 7 is executed, for example, when the parameter identification device 12 (FIG. 6) identifies parameters for the mechanical system 2 before starting the actual operation of the mechanical system 2.

First, the operator of the motor control system 1 operates a host device to output a predetermined angle command value, thereby causing the angle of the load 21 (load angle θ _L ) to be repeatedly operated with a small amplitude and a low frequency (step S01). ).

Next, the data acquisition unit 120 of the parameter identification device 12 determines the motor angular velocity ω _M , the motor angle θ _M , the load angular velocity ω _L, and the load angle θ _L from the mechanical system 2 that is repeatedly operating with a small amplitude and a low frequency. Actual measurement data indicating a dynamic change is acquired (step S02).

Next, the phase diagram generation unit 121 of the parameter identification device 12 generates a plurality of phase diagrams from the actual measurement data acquired in step S02 (step S03).

Here, as shown in FIG. 8, the phase diagram generator 121 measures the twist angle (θ _M −θ _L ) measured before and after the load angle θ _L reverses the direction, and measures the torque τ. The measured values sampled at the same time are linked and extracted. Here, the “measured data of the twist angle (θ _M −θ _L )” is obtained by subtracting the measured data of the load angle θ _L from the measured data of the motor angle θ _M. Then, based on the extracted actual measurement values, the phase diagram generator 121 is a phase diagram in which the horizontal axis represents the twist angle (θ _M −θ _L ) and the vertical axis represents the torque τ, as shown in FIG. A certain "twist angle-torque phase diagram P1" is generated.
It should be noted that the phase diagram generator 121 calculates the actual measurement data acquired before and after the load angle θ _L is reversed from the positive direction to the negative direction and the actual measurement data acquired before and after the reverse of the load angle θ _L from the negative direction to the positive direction. The measured values are linked to each other. In the torsion angle-torque phase diagram P1 thus generated, a hysteresis curve mainly due to the non-linear friction characteristic of the mechanical system 2 (see FIG. 3A) and the dead zone characteristic function F1 appears.

Similarly, the phase diagram generator 121 compares the actual measurement data of the torque τ before and after the direction reversal of the load angle θ _L and the actual measurement data of the motor angular velocity ω _M and the load angular velocity ω _L with each other at the same time. And link to extract. Then, based on the extracted actual measurement values, the phase diagram generator 121 is a phase diagram in which the horizontal axis represents torque τ and the vertical axis represents motor angular velocity ω _M and load angular velocity ω _L, as shown in FIG. A certain "torque-mechanical angular velocity phase diagram P2" is generated.
Similarly, the phase diagram generator 121 determines the same time from the measured data of the twist angle (θ _M −θ _L ) before and after the direction of the load angle θ _L is reversed and the measured data of the motor angular velocity ω _M and the load angular velocity ω _L. The actual measurement values sampled in are linked and extracted. Then, the phase diagram generator 121 sets the twist angle (θ _M −θ _L ) as the horizontal axis based on the extracted actual measurement value and sets the motor angular velocity ω _M and the load angular velocity ω _L to the vertical axis. A "twist angle-mechanical angular velocity phase diagram P3", which is a phase diagram for the axis, is generated.
Further, the phase diagram generator 121 compares the measured values of the torque τ before and after the direction of the load angle θ _L is reversed and the measured values of the torsional velocity (ω _M −ω _L ) with each other at the same time. Extract by linking. Here, the “measured data of the twisting speed (ω _M −ω _L )” is obtained by subtracting the measured data of the load angular velocity ω _L from the measured data of the motor angular velocity ω _M. Then, based on the extracted actual measurement values, the phase diagram generator 121 is a phase diagram in which the horizontal axis represents the torque τ and the vertical axis represents the twist speed (ω _M −ω _L ) as shown in FIG. A certain "torque-torsion velocity phase diagram P4" is generated.
Further, the phase diagram generator 121 determines the same time from the measured data of the twist angle (θ _M −θ _L ) and the measured data of the twist speed (ω _M −ω _L ) before and after the direction of the load angle θ _L is reversed. The sampled actual measurement values are linked and extracted. Then, the phase diagram generator 121 sets the twist angle (θ _M −θ _L ) on the horizontal axis and sets the twist velocity (ω _M −ω _L ) vertically based on the extracted actual measurement value. A "twist angle-torsion velocity phase diagram P5", which is a phase diagram for the axis, is generated.
Furthermore, phase plane view generating unit 121, the measured data of the load angle theta _L in the direction reversed before and after the load angle theta _L, and extracted in association measured values each other sampled at the same time from the measured data of the torque τ .. Then, based on the extracted actual measurement values, the phase diagram generator 121 is a phase diagram "load angle-" that shows the load angle θ _L on the horizontal axis and the torque τ on the vertical axis as shown in FIG. The torque phase diagram P6” is generated.

Next, the parameter identifying unit 122 selects the model motor-side friction coefficient τ _fM0 and the model load-side friction coefficient τ among the various model parameters forming the two-inertia system model MOD based on the various phase diagrams generated in step S03. _fL0, to identify the model dead zone width BL ₀ and model torsional rigidity coefficient _{K R0} (step S04). Specifically, the parameter identifying unit 122 identifies the model parameter based on the position of the break point in each phase diagram.

Here, based on the block diagram shown in FIG. 2, it is possible to obtain the equation (1) that is the equation of motion for the motor 20 and the equation (2) that is the equation of motion for the load 21.

In Expressions (1) and (2), “τ _fM[ωM] ” is a motor-side friction coefficient defined as a variable of the motor angular velocity ω _M based on the motor-side friction characteristic function G _M (FIG. 3B). τ _fM is shown. Further, “τ _{fL [ωL]} ” indicates a load side friction coefficient τ _fL defined as a variable of the load angular velocity ω _L based on the load side friction characteristic function G _{L.
Further, "BKLS [θM-θL]} " indicates the backlash displacement function F1 'backlash displacement BKLS defined as a variable twist angle (θ _M -θ _L) based on (Figure 4 (b)).

In the actual mechanical system 2, the torsional viscosity coefficient D _R of the shaft 22 has a small effect, so the torsional viscosity coefficient D _R is ignored in the equations (1) and (2).
In addition, in the above equations of motion (Equations (1) and (2)), when the repetitive operation of the mechanical system 2 is made to have a small amplitude and a low frequency, the inertia torque J _M s ² θ _{M of the} motor 20 and the inertia torque of the load 21 are The influences of J _L s ² θ _L , the viscous torque D _M sθ _{M of} the motor 20, and the viscous torque D _L sθ _L of the load 21 are also small. Therefore, by deleting these terms in the above equation of motion, the following equations (3) and (4) are derived.

From the equation (3), under the condition that the repetitive operation of the mechanical system 2 has a small amplitude and a low frequency, the torque τ output by the motor 20 according to the torque command value is the motor side friction coefficient τ _fM and the load side friction coefficient τ _fL . It is shown that it becomes the total value of.
Further, from the formula (4), under the condition that the repetitive operation of the mechanical system 2 has a small amplitude and a low frequency, the sum of the value obtained by dividing the load side friction coefficient τ _fL by the torsional rigidity coefficient K _R and the backlash displacement BKLS is twisted. It is shown that the angle is (θ _M −θ _L ). That is, the angle (displacement) obtained by subtracting the backlash displacement BKLS from twist angle (theta _M - [theta] _L) in the axial 22, a value obtained by multiplying a spring constant of the shaft 22 (torsional rigidity coefficient _{K R)} is the load side friction coefficient τ Match _fL .

Hereinafter, the respective break points T11 to T14 in the twist angle-torque phase diagram P1 shown in FIG. 9 will be described in consideration of the relationships of the expressions (3) and (4).

At the break point T11 to the break point T12 in the twist angle-torque phase diagram P1, the torque τ saturated in the negative direction is reversed in the positive direction, and accordingly, the motor 20 is rotationally driven in the positive direction. The locus until the start is shown. That is, in a process in which the torque τ that has been at the minimum value (saturated state in the negative direction) at the break point T11 gradually increases in the positive direction, the torque τ is a saturated value of friction (motor When the side Coulomb friction coefficient τ _fMc ) is exceeded, the motor 20 starts to rotate in the positive direction. On the other hand, at the time of the break point T12, the influence of the friction of the load 21 does not occur due to the presence of the dead zone (-BL to +BL). Therefore, the torque τ at the time of reaching the break point T12 matches the saturation value of the friction generated in the motor 20 (motor-side Coulomb friction coefficient τ _fMc ) from the equation (3).

In addition, from the twist point T12 to the turn point T13 in the twist angle-torque phase diagram P1, the twist angle (θ _M −θ _L ) of the shaft 22 reaches from the minimum value (−BL) of the dead zone to the maximum value (+BL). The locus of is shown. That is, from the break point T12 to the break point T13, only the backlash displacement BKLS changes, and the power is not transmitted to the load 21. Therefore, since only the motor 20 is driven, the torsion angle (θ _M −θ _L ) changes while the torque τ maintains the motor-side Coulomb friction coefficient τ _fMc constant. Therefore, the twist angle (θ _M −θ _L ) at the time when the break point T13 is reached matches the maximum value (+BL) of the dead zone from the equation (4).

In addition, the bending point T13 to the bending point T14 in the twist angle-torque phase diagram P1 are applied to the load 21 through the twist angle (θ _M −θ _L ) in which the backlash displacement BKLS is saturated in the positive direction and exceeds the dead zone width BL. The locus until the applied torque τ′ gradually increases and the load 21 starts rotational driving in the positive direction accordingly is shown. That is, in the process in which the torque τ that rotationally drives only the motor 20 at the break point T13 further increases in the positive direction, the torque τ is the saturation value of the friction generated in the motor 20 and the friction generated in the load 21. The load 21 starts to rotate in the positive direction at the time when the load 21 and the saturation value (load-side Coulomb friction coefficient τ _fLc ) are balanced. Therefore, the torque τ at the time of reaching the break point T14 matches the total value of the motor-side Coulomb friction coefficient τ _fMc and the load-side Coulomb friction coefficient τ _fLc from the equation (3).
Further, the twist angle (θ _M −θ _L ) at the time of reaching the break point T14 matches “τ _fLc /K _R +BL” from the equation (4).

Based on the above characteristics, the parameter identifying unit 122 identifies various model parameters based on the positions of the respective break points T11 to T14.
Specifically, the parameter identifying unit 122 identifies the model motor-side Coulomb friction coefficient τ _fMc0 by referring to the value on the vertical axis of the break point T12 (or the break point T13) in the torsion angle-torque phase diagram P1. Further, the parameter identifying unit 122 identifies the model load side Coulomb friction coefficient τ _fLc0 by subtracting the value of the previously identified model motor side Coulomb friction coefficient τ _fMc0 from the value on the vertical axis at the break point T14. Further, the parameter identifying unit 122 identifies the model dead zone width BL ₀ by referring to the value on the horizontal axis of the break point T13 (or the break point T12). Further, the parameter identifying unit 122 solves the model (4) by solving the equation (4) based on the value of the horizontal axis at the break point T14 and the model dead zone width BL ₀ and the model load side Coulomb friction coefficient τ _fLc0 identified earlier. The torsional stiffness coefficient K _R0 is identified.

Next, the respective break points T21 to T24 in the torque-mechanical angular velocity phase diagram P2 shown in FIG. 10 will be described while considering the relationships of the expressions (3) and (4).
At the break points T21 to T22 in the torque-mechanical angular velocity phase diagram P2, the torque τ saturated in the negative direction is inverted toward the positive direction, and accordingly, the torque τ is at the minimum value (negative saturation). It shows a locus until the angular velocities of the motor 20 and the load 21 become zero with almost no change from (state).
In addition, the break point T22 to the break point T23 are due to the existence of the dead zone (-BL to +BL) while the torque τ gradually increases in the positive direction from the state where the angular velocity of the motor 20 and the load 21 is zero. The locus in which only the angular velocity ω _M rises is shown.
Further, at the break points T23 to T24, the torque τ exceeds the torque required to drive only the motor 20 (motor-side Coulomb friction coefficient τ _fMc ), so that the torque τ′ is applied to the load 21 and the motor angular velocity ω _M And the load angular velocity ω _L rise equally.

Here, the value on the horizontal axis of the break point T22 can be _{calculated as} “ _{−τ fMc −τ fLc} ” from the equation (3) based on the fact that the motor angular velocity ω _M and the load angular velocity ω _L are zero.
The value on the horizontal axis of the break point T23 is the torque at the time when the angular velocity of the load 21 starts to increase (when only the motor 20 is rotationally driven). Therefore, from the formula (3), the value of the break point T23 on the horizontal axis matches the motor-side Coulomb friction coefficient τ _fMc .

The parameter identifying unit 122 subtracts the previously identified model motor-side Coulomb friction coefficient τ _fMc0 from the value on the horizontal axis of the break point T22 in the torque-mechanical angular velocity phase diagram P2 to obtain the model load-side Coulomb friction coefficient τ _fLc0. Identify.
Here, at the break point T14 of the twist angle-torque phase diagram P1, the torque τ and the twist angle (θ _M −θ _L ) are not completely saturated, and the motor side viscosity coefficient D _M and the load side viscosity coefficient D _L. It is known that an error component based on Then, if an attempt is _made to identify the model load side Coulomb friction coefficient τ _fLc0 based on the position of the break point T14 of the twist angle-torque phase diagram P1, it is conceivable that the accuracy will decrease due to the error component.
On the other hand, at the turning point T22 of the torque-mechanical angular velocity phase diagram P2, the motor angular velocity ω _M and the load angular velocity ω _L are zero. Therefore, at the break point T22, there is no influence of the motor-side viscosity coefficient D _M and the load-side viscosity coefficient D _L that give a component proportional to the motor angular velocity ω _M and the load angular velocity ω _L. Therefore, the model load side Coulomb friction coefficient τ _fLc0 can be identified with higher accuracy from the value of the horizontal axis of the break point T22.

Further, the parameter identifying unit 122 identifies the model motor-side Coulomb friction coefficient τ _fMc0 by referring to the value on the horizontal axis of the break point T23 in the torque-mechanical angular velocity phase diagram P2.

Next, the respective break points T31 to T34 in the twist angle-mechanical angular velocity phase diagram P3 shown in FIG. 11 will be described while considering the relationships of the expressions (3) and (4).
At the bending point T31 to the bending point T32 in the torsion angle-mechanical angular velocity phase diagram P3, the torque τ saturated in the negative direction reverses toward the positive direction, and accordingly, the torsion angle (θ _M −θ _L ) Shows a locus until the angular velocities of the motor 20 and the load 21 become zero while the value of () hardly changes from the minimum value (negative saturation state).
Further, at the break points T32 to T33, only the motor angular speed ω _M increases while the load angular speed ω _L remains zero due to the presence of the dead zone (−BL to +BL), and thus the twist angle (θ _M −θ _L ) is increased. It shows a trajectory that gradually increases.
In addition, at the break points T33 to T34, the torque τ′ is applied to the load 21 when the twist angle (θ _M −θ _L ) exceeds the maximum value (+BL) of the dead zone, and the motor angular velocity ω _M and the load angular velocity ω are applied. _It shows a locus in which _L and _L rise equally.

Here, the value on the horizontal axis of the break point T32 is calculated based on that the load angular velocity ω _L is zero and the backlash displacement BKLS is “−BL” (θ _M −θ _L <−BL). (4) than can be obtained with _{"-τ fLc / K R -BL"} .
The value on the horizontal axis of the break point T33 is the twist angle (θ _M −θ _L ) at the time when the angular velocity of the load 21 starts to rise (at the time when the load angular velocity ω _L is zero). Therefore, the value on the horizontal axis of the break point T33 matches the maximum value (+BL) of the dead zone according to the equation (4).

The parameter identifying unit 122 calculates the equation based on the value of the horizontal axis of the break point T32 in the torsion angle-mechanical angular velocity phase diagram P3, the model dead zone width BL ₀ and the model load side Coulomb friction coefficient τ _fLc0 identified earlier. By solving 4), the model torsional stiffness coefficient K _R0 is identified.
In addition, the parameter identifying unit 122 identifies the model dead zone width BL ₀ by referring to the value on the horizontal axis of the bending point T33 in the twist angle-mechanical angular velocity phase diagram P3.

Next, the respective break points T41 to T43 derived from the torque-torsion velocity phase front view P4 shown in FIG. 12 will be described.
The torque-torsion velocity phase diagram P4 shown in FIG. 12 is a phase diagram showing the difference between the motor angular velocity ω _M and the load angular velocity ω _L shown in the torque-mechanical angular velocity phase diagram P2 (FIG. 10). Therefore, the position of the break point T41 in the torque-torsion velocity phase diagram P4 corresponds to the position of the break point T21 in the torque-mechanical angular velocity phase diagram P2. Further, the position of the break point T42 in the torque-torsion velocity phase diagram P4 corresponds to the position of the break point T23 in the torque-mechanical angular velocity phase diagram P2. Furthermore, the position of the break point T43 in the torque-torsion velocity phase diagram P4 corresponds to the position of the break point T24 in the torque-mechanical angular velocity phase diagram P2.

The parameter identifying unit 122 identifies the model motor-side Coulomb friction coefficient τ _fMc0 by referring to the value on the horizontal axis of the break point T42 in the torque-torsion velocity phase diagram P4.
Here, as shown in FIG. 12, the break point T42 in the torque-torsion velocity phase front view P4 is more clearly shown than the break point T23 in the torque-mechanical angular velocity phase front view P2 (FIG. 10). Therefore, the model motor-side Coulomb friction coefficient τ _fMc0 can be identified with higher accuracy.

Next, the respective break points T51 to T53 derived from the twist angle-twist velocity phase front view P5 shown in FIG. 13 will be described.
A twist angle-torsion velocity phase diagram P5 shown in FIG. 13 is a phase diagram showing a difference between the motor angular velocity ω _M and the load angular velocity ω _L shown in the twist angle-mechanical angular velocity phase diagram P3 (FIG. 11). .. Therefore, the position of the break point T51 in the twist angle-torsion velocity phase diagram P5 corresponds to the position of the break point T31 in the twist angle-mechanical angular velocity phase diagram P3. Further, the position of the break point T52 in the twist angle-torsion velocity phase diagram P5 corresponds to the position of the break point T32 in the twist angle-mechanical angular velocity phase diagram P3. In addition, the position of the break point T53 in the twist angle-torsion velocity phase diagram P5 corresponds to the position of the break point T33 in the twist angle-mechanical angular velocity phase diagram P3. Further, the position of the break point T54 in the twist angle-torsion velocity phase diagram P5 corresponds to the position of the break point T34 in the twist angle-mechanical angular velocity phase diagram P3.

The parameter identifying unit 122 identifies the model dead zone width BL ₀ with reference to the value on the horizontal axis of the bending point T53 in the twist angle-torsion velocity phase diagram P5.
Further, the parameter identifying unit 122 is based on the value of the horizontal axis of the break point T52 in the twist angle-torsion velocity phase diagram P5, and the model dead zone width BL ₀ and the model load side Coulomb friction coefficient τ _fLc0 identified previously. By solving the equation (4), the model torsional rigidity coefficient K _R0 is identified.
Here, as shown in FIG. 13, the break point T52 and the break point T53 in the twist angle-torsion velocity phase plane P5 are clearer than the break points T32 and T33 in the twist angle-mechanical angular velocity phase plan P3 (FIG. 11). Is shown in. Therefore, the model dead zone width BL ₀ and the model torsional rigidity coefficient K _R0 can be identified more accurately.

Next, the break point T61 and the break point T63 derived from the load angle-torque phase diagram P6 shown in FIG. 14 will be described.
At the time points of the break points T61 and T63, both the motor side friction coefficient τ _fM and the load side friction coefficient τ _fL are in a saturated state. Therefore, from the equation (3), the vertical axis value of the break point T61 can be _{calculated as} “ _{−τ fMc −τ fLc} ”, and the vertical axis value of the break point T63 can be _{calculated as} “τ _fMc +τ _fLc ”.

The parameter identifying unit 122 subtracts the previously identified model motor-side Coulomb friction coefficient τ _fMc0 from the value on the vertical axis of the break point T63 in the load angle-torque phase diagram P6 to obtain the model load-side Coulomb friction coefficient τ _fLc0. Identify.
Here, as shown in FIG. 14, the break point T63 in the load angle-torque phase plan P6 is more clearly shown than the break point T14 in the twist angle-torque phase plan P1 (FIG. 9). Therefore, the model load side Coulomb friction coefficient τ _fLc0 can be identified with higher accuracy.

The parameter identification unit 122 according to the present embodiment uses, for example, the model motor side friction coefficient τ _fM0 identified from the break point T42 of the torque-torsion speed phase diagram P4 and the break point T22 of the torque-mechanical angular velocity phase diagram P2. Adopt the identified model load side friction coefficient τ _fL0 . Further, the parameter identifying unit 122 employs, for example, the model dead zone width BL ₀ and the model torsional rigidity coefficient K _R0 identified from each of the break points T52 and T53 of the twist angle-twist velocity phase diagram P5.
This makes it possible to identify any model parameter with high accuracy.

The parameter identifying unit 122 substitutes the identified model motor-side Coulomb friction coefficient τ _fMc0 into the motor-side friction characteristic function G _M (see FIG. 3B). The parameter identification unit 122, by multiplying a predetermined coefficient to model motor side Coulomb friction coefficient tau _FMc0, various constants (slope _K 1 constituting the motor side friction characteristic function _{G M, K 2,} K ₃ , K ₄ , offsets τ _fM1 , τ _fM2 , τ _fM3 , τ _fM4 , τ _fM5 ) are identified. Thereby, the parameter identifying unit 122 identifies the model motor side friction coefficient τ _fM0 .
Similarly, the parameter identifying unit 122 substitutes the identified model load side Coulomb friction coefficient τ _fLc0 into the additional side friction characteristic function G _L. Then, the parameter identifying unit 122 identifies various constants (slope, offset) that configure the load-side friction characteristic function G _L by multiplying the model load-side Coulomb friction coefficient τ _fLc0 by a predetermined coefficient. Accordingly, the parameter identifying unit 122 identifies the model load side friction coefficient τ _fL0 .

The parameter identifying unit 122 according to the present embodiment performs PSO (Particle Swarm Optimization) or the like on the generated various phase diagrams to minimize the error area from the hysteresis curve shown in the front view. Each broken point is detected by performing such a polygonal line approximation calculation.

(Action, effect)
As described above, the parameter identification device 12 according to the first embodiment has the above-described data acquisition unit 120, the phase diagram generation unit 121, and the parameter identification unit 122.
By doing so, the motor side friction coefficient τ _fM , the load side friction coefficient τ _fL , the dead zone width BL, and the torsional rigidity coefficient K _{R of the} mechanical system 2 are _calculated from the plurality of break points represented in the plurality of phase diagrams. Can be separately and independently evaluated to accurately identify the model parameters corresponding to the various parameters.
Therefore, it is possible to obtain the two-inertia system model MOD that accurately simulates the mechanical system 2 including the motor 20 and the load 21.

Further, the phase diagram generator 121 according to the first embodiment shows, as a phase diagram, a torque-torsion velocity phase diagram P4 showing the relationship between the torque τ and the torsion velocity (ω _M −ω _L ) of the shaft 22. To generate. Then, the parameter identifying unit 122 identifies a model parameter (model motor side friction coefficient τ _fM0 ) indicating the friction of the motor 20 based on the position of the break point in the torque-torsion velocity phase diagram P4.
By doing so, the model motor side friction coefficient τ _fM0 can be identified with higher accuracy.

Further, the phase diagram generating unit 121 according to the first embodiment, as a phase diagram, shows the relationship between the twist angle (θ _M −θ _L ) of the shaft 22 and the twist speed (ω _M −ω _L ) of the shaft 22. A twist angle-twist velocity phase surface diagram P5 is generated. Then, the parameter identifying unit 122 determines the rigidity of the shaft 22 based on the position of the break point in the twist angle-torsion velocity phase diagram P5, and model parameters indicating the dead zone width (model twist stiffness coefficient K _R0 , model dead zone width BL). ₀ ) is identified.
By doing so, the model torsional rigidity coefficient K _R0 and the model dead zone width BL ₀ can be identified with higher accuracy.

Further, the phase diagram generating unit 121 according to the first embodiment generates a torque-mechanical angular velocity phase diagram P2 showing the relationship between the torque τ and the angular velocity of the motor 20 or the angular velocity of the load 21 as the phase diagram. .. Then, the parameter identifying unit 122 identifies the model parameter (model load side friction coefficient τ _fL0 ) indicating the friction of the load 21 based on the position of the break point in the torque-mechanical angular velocity phase diagram P2.
By doing so, the model load side friction coefficient τ _fL0 can be identified with higher accuracy.

Further, the phase diagram generating unit 121 according to the first embodiment generates a load angle-torque phase diagram P6 showing the relationship between the angle of the load 21 (load angle θ _L ) and the torque τ as the phase diagram. To do. Then, the parameter identifying unit 122 identifies a model parameter (model load side friction coefficient τ _fL0 ) indicating the friction of the load 21 based on the position of the break point in the load angle-torque phase diagram P6.
By doing so, the model load side friction coefficient τ _fL0 can be identified with higher accuracy.

Further, the phase diagram generator 121 according to the first embodiment shows, as a phase diagram, a twist angle-torque phase diagram P1 showing the relationship between the twist angle (θ _M −θ _L ) of the shaft 22 and the torque τ. To generate. Then, the parameter identifying unit 122 determines which of the friction of the motor 20, the friction of the load 21, the rigidity of the shaft 22, and the dead band width of the shaft 22 based on the position of the break point in the torsion angle-torque phase diagram P1. A model parameter indicating at least one is identified.
By doing so, the model motor side friction coefficient τ _fM0 , the model load side friction coefficient τ _fL0 , the model torsional rigidity coefficient K _R0, and the model dead zone width BL ₀ can be identified from one phase diagram.

Although the motor control system 1 and the parameter identification device 12 according to the first embodiment have been described above in detail, the specific modes of the motor control system 1 and the parameter identification device 12 are not limited to those described above. It is possible to add various design changes and the like without departing from the gist of the invention.

For example, when a plurality of values are identified for the same type of model parameters that can be identified from a plurality of phase diagrams (P1 to P6), the parameter identification unit 122 according to another embodiment determines the average value thereof. It may be a model parameter. For example, the parameter identification unit 122 averages the model dead zone width BL ₀ identified from the twist angle-torque phase plane P1 and the model dead zone width BL ₀ identified from the twist angle-torsion velocity phase plane P5. May be the model dead zone width BL ₀ adopted in the two-inertia model MOD.

The phase diagram generation unit 121 according to another embodiment may have a mode in which only one phase diagram (twist angle-torque phase diagram P1) is generated. In this case, the parameter identifying unit 122 determines the model motor-side friction coefficient τ _fM0 , the model load-side friction coefficient τ _fL0 , the model torsional rigidity coefficient K _R0, and the model dead zone width BL _{0 from} the generated twist angle-torque phase diagram P1. Identify all.
By doing so, the identification process of each model parameter can be simplified.

Further, the phase diagram generator 121 according to another embodiment repeats the acquisition of the actual measurement data a plurality of times for each repetitive operation of the mechanical system 2 and generates each phase diagram based on the averaged result. Good.
By doing so, the variation error of the actually measured data is reduced, so that the identification accuracy of the model parameter can be further improved.

Further, the parameter identification device 12 according to the first embodiment can be used not only for the feedforward function in the motor control system 1, but also for abnormality diagnosis and machine adjustment of the motor control system 1.

In the first embodiment, the parameter identification device 12 has been described as performing the above-described various processes with the torque command value input from the higher-level device as “torque τ”. It is not limited to the embodiment.
For example, the parameter identification device 12 according to another embodiment performs the above-described various processes by using, as the “torque τ”, the “measured torque” obtained from the detected current value flowing in the motor 20, instead of the “torque command value”. It may be a mode.

Further, when there is a steady disturbance such as gravity, the torque τ and the twist angle (θ _M −θ _L ) are given a constant offset. Therefore, the parameter identifying device 12 according to another embodiment further has a function of obtaining the average of the torque τ and the twist angle (θ _M −θ _L ) to remove the offset amount, and a function of averaging the breaking points with positive and negative values. You may have.

<Second Embodiment>
Next, a motor control system according to the second embodiment will be described in detail with reference to FIGS.

(Function configuration)
FIG. 15 is a diagram showing a functional configuration of the parameter identification device according to the second embodiment.
As shown in FIG. 15, the parameter identification device 12 according to the second embodiment further includes a motor side error system 123 and a load side error system 124.
The motor side error system 123 is a model of the two inertia system model MOD on the motor side based on the torque τ, the motor angle θ _M , and the load angle θ _L calculated by the feedback control unit 10 and the feedforward control unit 11. The motor-side error signal e _M indicating the degree of error with respect to the motor 20 is output.
The load-side error system 124 also outputs a load-side error signal e _L indicating the degree of error of the load-side model of the two-inertia model MOD based on the motor angle θ _M and the load angle θ _L. Output.
Further, the parameter identifying unit 122 according to the present embodiment inputs the motor-side error signal e _M, and the model parameter group (J _M0 , D _M0 , τ _fM0 , which is a model on the motor side of the two-inertia system model MOD, BL ₀ ) is identified. Further, the parameter identifying unit 122 inputs the load-side error signal e _L and identifies the model parameters (J _L0 , D _L0 , τ _fL0 , BL ₀ ) forming the load-side model in the two-inertia system model MOD. ..

FIG. 16 is a first diagram illustrating a process of the motor side error system and the load side error system according to the second embodiment.
The motor side error system 123 inputs the torque τ, the motor angle θ _M, and the load angle θ _L , and calculates the motor side error signal e _M according to the block diagram shown in FIG. 16.
Here, each of the model parameter groups (J _M0 , D _M0 , τ _fM0 , BL ₀ ) forming the model on the motor side is an unknown parameter group (J _M , D _M , τ _fM ) indicating the actual characteristics of the motor 20. , BL), the position of the motor 20 rotated by the torque τ calculated based on the model parameter group (motor angle θ _M ) is the assumed position of the motor 20. It should deviate from the (target motor angle). Therefore, the degree of error between the torque that should originally be applied to the motor 20 to reach the assumed target motor angle and the torque τ that is actually applied to the motor 20 is determined by the motor-side error signal e _M. When expressed by, the equation shown in the equation (5) is established from the input/output relation of the portion related to the motor 20 in the block diagram shown in FIG.

The motor side error system 123 according to this embodiment calculates the motor side error signal e _M based on the equation (5). Here, in the block diagram shown in FIG. 16, the part corresponding to the motor side error system 123 is a block diagram corresponding to a modified formula of the formula (5). The motor side error system 123 receives the input torque τ and motor angle θ _M according to the block diagram shown in FIG. 16 and the model parameters (J _M0 , D _M0 , τ _fM0 , BL ₀ ) given in advance. And the motor side error signal e _M is calculated based on the load angle θ _L.

Here, sign(sθ _M ) (the sign function S1 shown in FIG. 16) takes a value of “+1” when sθ _M is positive (sθ _M >0), and sθ _M is negative (sθ _M <0). Is a non-linear function that takes a value of "-1" when.
Further, BKLS(θ _M −θ _L ) (dead zone characteristic function F1 shown in FIG. 16) is of a system (model simulating the shaft 22) in which power is transmitted from the motor side to the load side in the two-inertia system model MOD. It is a non-linear function whose characteristics are represented by the model dead band width BL ₀ . Specifically, when there is "play" in the shaft 22 provided between the motor 20 and the load 21, the deviation between the motor angle θ _M and the load angle θ _L is within the range of the "play". As long as the torque generated from the motor 20 is not transmitted to the load 21. The model dead band width BL ₀ is a value that defines the width of this “play” (dead band width BL) as a model, and the dead band characteristic function F 1 has the absolute value of the deviation (θ _M −θ _L ) as the model dead band width BL _0. If the absolute value of the deviation (θ _M −θ _L ) exceeds the model dead zone width BL ₀ in the following cases, “θ _M −θ _L −BL ₀ ”(θ _M −θ _L >0) or It is a non-linear function that takes a value of “θ _M −θ _L +BL ₀ ”(θ _M −θ _L <0).

Load error system 124 receives the motor angle theta _M and the load angle theta _L, according to the block diagram shown in FIG. 16, and calculates the load side error signal e _L.
Here, each of the model parameter groups that form a model of the load side _{(J L0, D L0, τ} fL0, BL 0) is a parameter group _(J L indicating the actual characteristics of the load _{21, D L, τ fL,} BL ), the load angle (load angle θ _L ) rotationally moved by the torque τ calculated based on the model parameter group is the assumed load angle (target angle θ _t ). Get off. Therefore, in order to reach the assumed load angle θ _t , the error between the torque that should have originally been applied to the load 21 (via the shaft 22) and the torque τ′ actually applied to the load 21. Is expressed by the load-side error signal e _L , the equation shown in Expression (6) is established from the input/output relationship of the portion related to the load 21 in the block diagram shown in FIG.

The load side error system 124 according to the present embodiment calculates the motor side error signal e _M based on the equation (6). Here, in the block diagram shown in FIG. 16, the part corresponding to the load-side error system 124 is a block diagram corresponding to a modified formula (6). The load-side error system 124 uses the block diagram shown in FIG. 16 and the input motor angle θ _M and load angle according to the model parameters (J _L0 , D _L0 , τ _fL0 , BL ₀ ) given in advance. The load-side error signal e _L is calculated based on θ _L.

Here, sign(sθ _L ) (the sign function S2 shown in FIG. 16) takes a value of “+1” when sθ _L is positive (sθ _L >0), and sθ _L is negative (sθ _L <0). Is a non-linear function that takes a value of "-1" when.

FIG. 17 is a second diagram illustrating the processing of the motor-side error system and the load-side error system according to the second embodiment.
Here, each of the model parameter groups (J _M0 , D _M0 , τ _fM0 , J _L0 , D _L0 , τ _fL0 , BL ₀ ) and the parameter groups indicating the actual characteristics of the mechanical system 2 (J _M , D _M , _{τ fM, J L, D L} , τ fL, the degree of error between the respective BL), respectively, the motor-side inertia moment error component .delta.j _M, the motor-side viscosity coefficient error component [delta] D _M, the motor-side friction factor error represents components .DELTA..tau _fM, load inertia moment error component .delta.j _L, load side viscosity coefficient error component [delta] D _L, the load side friction coefficient error component .DELTA..tau _fL and the dead zone width error component [Delta] BL, defined as in equation (7).

Then, the motor-side error signal e _M and the load-side error signal e _L calculated based on the block diagram shown in FIG. 16, based on the block diagram shown in FIG. 17, the error component parameter group (.delta.j _{M, δD M, δτ fM, δJ L} , δD L, δτ fL, can be represented by [Delta] BL).

Specifically, the motor-side error signal e _M is an error corresponding to each of a plurality of model parameter groups (J _M0 , D _M0 , τ _fM0 , BL ₀ ) forming a motor-side model in the two-inertia system model MOD. is the component motor side error parameter group _{(δJ M, δD M, δτ} fM, δBL) based on, can be expressed as equation (8).

Similarly, the load-side error signal e _L is an error component corresponding to each of a plurality of model parameter groups (J _L0 , D _L0 , τ _fL0 , BL ₀ ) forming a load-side model in the two-inertia system model MOD. there load error parameter group _{(δJ L, δD L, δτ} fL, δBL) based on, can be expressed by equation (9).

Here, the dead zone width error component δBL is an error component corresponding to a model parameter (model dead zone width BL ₀ ) indicating the dead zone width BL, that is, the model dead zone width BL ₀ and the mechanical system 2 defined as the two-inertia system model MOD. Is an error from the actual dead band width BL in.
Further, ∂BKLS(θ _M −θ _L )/∂BL (dead zone characteristic change function F2 shown in FIG. 17) is a non-linearity indicating the degree of change that the dead zone width error component δBL gives to the characteristic of the above-mentioned dead zone characteristic function F1. Is a function. Details of the dead zone characteristic change function F2 will be described later.

In the block diagram shown in FIG. 17, the input motor angle θ _M and load angle θ _L, and the output motor-side error signal e _M and load-side error signal e _L are the processes described with reference to FIG. Has been already known.
The motor side error system 123 according to the present embodiment linearly associates each of the motor side error parameter groups (δJ _M , δD _M , δτ _fM , δBL) with the motor side error signal e _{M. It} outputs _0M , q _1M , q _2M and dead band internal signal q ₃ .
Similarly, the load-side error system 124 according to the present embodiment is such that the load-side error parameter group (δJ _L , δD _L , δτ _fL , δBL) is linearly associated with the load-side error signal e _L. The internal signals q _0L , q _1L , q _2L and the dead zone internal signal q ₃ are output.
Note that the dead zone internal signal q ₃ dead zone of the dead zone for the internal signal q ₃ and the load-side error system 124 outputs the motor-side error system 123 outputs is identical, for convenience of explanation, the motor-side error system 123 outputs The internal signal q ₃ for use is also referred to as a motor-side internal signal q _3, and the dead band internal signal q ₃ output by the load-side error system 124 is also referred to as a load-side internal signal q ₃ .

Here, the motor-side internal signals q _0M , q _1M , q _2M , and q ₃ are respectively expressed by the formula (() based on the input motor angle θ _M , load angle θ _L , and the block diagram shown in FIG. 10).

Each of the motor-side internal signals q _0M , q _1M , q _2M , and q ₃ calculated in this way is calculated from each of the motor-side error parameter groups (δJ _M , δD _M , δτ _fM , δBL) and the motor-side error. It is a parameter that linearly associates the signal e _M with.

Further, the load-side internal signals q _0L , q _1L , q _2L , and q ₃ are respectively expressed by equation (11) based on the input motor angle θ _M , load angle θ _L , and the block diagram shown in FIG. ) Is represented.

Each of the load-side internal signals q _0L , q _1L , q _2L , and q ₃ calculated in this way is equal to each of the load-side error parameter groups (δJ _L , δD _L , δτ _fL , δBL) and the load-side error. It is a parameter that linearly associates the signal e _L with the signal e _L.

FIG. 18 is a third diagram illustrating the processing of the motor side error system and the load side error system according to the second embodiment.
As described above, the motor-side error system 123 and the load-side error system 124 according to the present embodiment output the dead band internal signal q ₃ as shown in FIG. The change function F2 is used. As described above, the dead zone characteristic change function F2 indicates the degree of change that the dead zone width error component δBL gives to the transfer characteristic (characteristic of the dead zone characteristic function F1) of the system in which power is transmitted from the motor 20 side to the load 21 side. It is a defined non-linear function.

Here, the "play" width (dead band width BL) of the shaft 22 provided between the motor 20 and the load 21 changes from the model dead band width BL ₀ by a predetermined dead band width error component δBL (δBL>0). Consider the case. In this case, in the dead zone characteristic function F1, as shown in FIG. 18A, the region where the output is zero with respect to the input (θ _M −θ _L ) expands by +δBL and −δBL in the positive and negative directions. As a result, in the region (θ _M −θ _L >BL ₀ ) where the input (θ _M −θ _L ) is larger than +BL ₀ , the output (vertical axis) changes by −δBL. Further, in the region (θ _M −θ _L <−BL ₀ ) where the input (θ _M −θ _L ) is smaller than −BL ₀ , the output (vertical axis) changes by +δBL.

Further, in the dead band characteristic function F1, when the dead band width BL is BL ₀ , the output increases when the input (θ _M −θ _L ) exceeds BL ₀ , whereas the dead band width BL ( When changes to BL ₀ + δBL), until the input (θ _M -θ _L) is greater than (BL ₀ + δBL), the output remains "zero". Therefore, in the range of the input (θ _M −θ _L ) from BL ₀ to (BL ₀ +δBL), the input (θ _M −θ _L ) and the degree of change in the negative direction of the output have a proportional relationship. is there. Similarly, in the range of the input (θ _M −θ _L ) from −BL ₀ to (−BL ₀ −δBL), the input (θ _M −θ _L ) and the degree of change in the positive direction of the output, Are in a proportional relationship.
In the region where the input (θ _M −θ _L ) is from BL ₀ to −BL ₀ , the output remains “zero” regardless of the change in the dead band width BL from BL ₀ to (BL ₀ +δBL). .. That is, even if the dead zone width BL changes from BL ₀ to (BL ₀ +δBL), the output does not change.

From the above, the dead zone characteristic change function F2, which represents the degree of change that the dead zone width error component δBL gives to the characteristic of the dead zone characteristic function F1, can be represented by the non-linear characteristic as shown in FIG.
That is, in the region where the input (θ _M −θ _L ) exceeds BL ₀ +δBL′, the output by the dead zone characteristic function F1 changes in the negative direction, so the dead zone characteristic change function F2 outputs “−1”. Similarly, in the region where the input (θ _M −θ _L ) is lower than −BL ₀ −δBL′, the output by the dead zone characteristic function F1 changes in the positive direction, so the dead zone characteristic change function F2 outputs “+1”. To do.
Further, in the region where the input (θ _M −θ _L ) is from BL ₀ to (BL ₀ +δBL′), the output is “zero” to “−1” as the input (θ _M −θ _L ) is increased. Change to gradually decrease until. Similarly, in the region where the input (θ _M −θ _L ) is from −BL ₀ to (−BL ₀ −δBL′), the output changes from “zero” as the input (θ _M −θ _L ) decreases. It changes so as to gradually increase to “+1”.
Then, in the region where the input (θ _M −θ _L ) is from BL ₀ to −BL ₀ , the output of the dead zone characteristic function F1 does not change, so the dead zone characteristic change function F2 outputs “zero”.
It should be noted that the constant δBL′ is a constant that is determined by predicting an assumed dead band width error component δBL in advance, and is set to, for example, about 20% of the model dead band width BL ₀ (δBL′=0.2×BL ₀ ). It

Next, the function of the parameter identifying unit 122 (see FIG. 15) according to the present embodiment will be described.
The parameter identification unit 122 calculates the motor-side error signal e _M calculated by the motor-side error system 123 (see FIG. 16) and the motor-side internal signals q _0M , q _1M , q _2M calculated by the motor-side error system 123. , Q ₃ (see FIG. 17).

The parameter identifying unit 122 determines that each of the motor-side internal signals q _0M , q _1M , q _2M , and q ₃ is an unknown motor-side error parameter group (δJ _M , δD _M , δτ _fM , δBL), and the motor-side. Each of the unknown motor-side error parameter groups is calculated by utilizing the fact that the error signal e _M and the error signal e _M are linearly associated with each other as shown in Expression (12).

Here, the parameter identifying unit 122 acquires the motor-side error signal e _M and the motor-side internal signals q _0M , q _1M , q _2M , and q ₃ that correspond to one cycle T of the periodic operation in the mechanical system 2. Then, both sides of the equation (12) are multiplied by the motor-side internal signals q _0M , q _1M , q _2M , and q ₃ , and both sides are time-integrated by one cycle T, which is transformed into the equation (13). .

Further, equation (13) is transformed into equation (14).

Thereby, the parameter identification unit 122 can calculate the unknown motor-side error parameter group (δJ _M , δD _M , δτ _fM , δBL) according to the equation (15).

Parameter identification unit 122, the calculated motor side error parameter group _{(δJ M, δD M, δτ} fM, δBL) each, the model parameter groups constituting the motor side of the model _{(J M0, D M0, τ} fM0, BL ₀ ) to identify a new group of model parameters (J _M0 , D _M0 , τ _fM0 , BL ₀ ).

Further, the parameter identifying unit 122 calculates the motor-side error signal e _L calculated by the load-side error system 124 (see FIG. 16) and the load-side internal signals q _0L , q _1L similarly calculated by the load-side error system 124. q _2L and q ₃ (see FIG. 17) are input.

The parameter identifying unit 122 determines that each of the load-side internal signals q _0L , q _1L , q _2L , and q ₃ is an unknown load-side error parameter group (δJ _L , δD _L , δτ _fL , δBL) and the load-side. Each of the unknown load-side error parameter groups is calculated by utilizing the fact that the error signal e _L and the error signal e _L are linearly associated with each other as in Expression (16).

Here, the parameter identifying unit 122 acquires the load-side error signal e _L and the load-side internal signals q _0L , q _1L , q _2L , and q ₃ that correspond to one cycle T of the periodic operation in the mechanical system 2. Then, both sides of the equation (16) are multiplied by the load-side internal signals q _0L , q _1L , q _2L , and q ₃ , and both sides are time-integrated by one cycle T, which is transformed into the equation (17). ..

Further, the equation (17) is transformed into the equation (18).

Thereby, the parameter identifying unit 122 can calculate the unknown load-side error parameter group (δJ _L , δD _L , δτ _fL , δBL) according to the equation (19).

Parameter identification unit 122, the calculated load error parameter group _{(δJ L, δD L, δτ} fL, δBL) respectively, the model parameter groups that form a model of predefined have load side _{(J L0, D L0} , Τ _fL0 , BL ₀ ), a new model parameter group (J _L0 , D _L0 , τ _fL0 , BL ₀ ) is identified.

The parameter identification unit 122 according to the present embodiment uses the same one cycle T as the motor side error signal e _M and the load side error signal e _L for one cycle T of the periodic operation in the mechanical system 2. The internal signals q _0M , q _1M , q _2M , q _0L , q _1L , q _2L , and q ₃ are acquired, and the relational expressions (Expression (12) and Expression (16)) between the error signal and the error parameter group are calculated. Time integration is performed in one cycle T.
By doing so, an unknown disturbance can be canceled by the time integration of one cycle T, so that the error parameter group (δJ _M , δD _M , δτ _fM , δJ _L , δD _L , δτ _fL , δBL) can be accurately measured. It can be calculated.

FIG. 19 is a diagram showing a processing flow of the parameter identifying device according to the second embodiment.
In the process flow shown in FIG. 19, for example, as in the first embodiment (FIG. 7), the parameter identification device 12 (FIG. 15) sets the parameters for the mechanical system 2 before the actual operation of the mechanical system 2 is started. It is executed when identification is performed.

The parameter identifying device 12 performs the model motor based on various measured data (ω _M , θ _M , ω _L , θ _L , τ) acquired during the repetitive operation of small amplitude and low frequency through steps S01 to S04. The side friction coefficient τ _fM0 , the model load side friction coefficient τ _fL0 , the model dead zone width BL ₀ , and the model torsional rigidity coefficient K _R0 are identified. Since each processing of step S01 to step S04 shown in FIG. 19 is the same as that of the first embodiment (FIG. 7), description thereof will be omitted.

Next, the operator of the motor control system 1 operates the host device to output a predetermined angle command value, thereby repeatedly operating the angle of the load 21 (load angle θ _L ) with a large amplitude and a high frequency (step S11).

Next, the data acquisition unit 120 of the parameter identification device 12 determines the motor angular velocity ω _M , the motor angle θ _M , the load angular velocity ω _L, and the load angle θ _L from the mechanical system 2 that is repeatedly operating at large amplitude and high frequency. The actual measurement data indicating the dynamic change is acquired (step S12).

Next, the motor-side error system 123 of the parameter identification device 12 performs the processing described with reference to FIGS. 16 to 18, and outputs the motor-side error signal e _M , the motor-side internal signals q _0M , q _1M , q _2M, and the dead zone. The internal signal q ₃ is output. In addition, the load side error system 124 of the parameter identification device 12 performs the load side error signal e _L , the load side internal signals q _0L , q _1L , q _2L, and the dead zone internal through the processing described in FIGS. 16 to 18. The signal q ₃ is output (step S13).

Next, the parameter identifying unit 122, based on various error signals and internal signals input from the motor-side error system 123 and the load-side error system 124, a motor-side error parameter group (δJ _M , δD _M , δτ _fM , δBL). , And a load-side error parameter group (δJ _L , δD _L , δτ _fL , δBL). Then, the parameter identifying unit 122 identifies the remaining model parameters that have not been identified in step S04 based on these error parameter groups (step S14).
Specifically, the parameter identifying unit 122 identifies the model motor side inertia moment J _M0 , the model motor side viscosity coefficient D _M0 , the model load side inertia moment J _L0 , and the model load side viscosity coefficient D _L0 in step S14. To do.

(Action, effect)
As described above, the parameter identification device 12 according to the second embodiment performs the identification when the amplitude and frequency of the repetitive operation performed by the mechanical system 2 are relatively small amplitude and low frequency; The model parameter is identified by dividing into a second step of performing identification when the amplitude and frequency of the repetitive operation performed by the system 2 are relatively large amplitude and high frequency.
More specifically, the parameter identifying unit 122 determines the motor based on the torque τ acquired in the first step, the actual measurement data of the angle and angular velocity of the motor 20, and the actual measurement data of the angle and angular velocity of the load 21. Model parameters indicating friction, load friction, shaft 22 stiffness, and shaft 22 deadband width are identified.
Then, the parameter identifying unit 122 determines the moment of inertia and the viscosity coefficient of the motor 20 based on the torque τ acquired in the second step, the actual measurement data of the angle of the motor 20, and the actual measurement data of the angle of the load 21. Indicated model parameters (model motor side inertia moment J _M0 , model motor side viscosity coefficient D _M0 ) and model parameters indicating the inertia moment and viscosity coefficient of the load 21 (model load side inertia moment J _L0 and model load side viscosity) The coefficient D _L0 ) is identified.

Here, in the state of small amplitude and low frequency, the influence of the inertia moment and the viscosity coefficient is small, and the characteristics of the friction of the motor 20 and the load 21, the rigidity of the shaft 22 and the dead band width can be easily captured. On the other hand, in the state of large amplitude and high frequency, the influences of the friction of the motor 20 and the load 21, the rigidity of the shaft 22 and the dead zone width are reduced, and the characteristics of the moment of inertia and the viscosity coefficient can be easily captured.
Therefore, by adopting a mode in which the above-described two-step steps are performed, it is possible to identify with high accuracy all the parameters forming the two-inertia system model MOD.

In each of the above-described embodiments, a program for realizing the function of the parameter identification device 12 in the motor control system 1 is recorded in a computer-readable recording medium, and the program recorded in this recording medium is recorded in the computer system. It is supposed that each procedure is carried out by loading and executing. Here, each process of the above-described parameter identifying device 12 is stored in a computer-readable recording medium in the form of a program, and the various processes are performed by the computer reading and executing the program. Here, the computer-readable recording medium refers to a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like. Further, the computer program may be distributed to the computer via a communication line, and the computer that receives the distribution may execute the program.
Further, the function of the parameter identifying device 12 may be provided in a plurality of devices connected by a network.

Although some embodiments of the present invention have been described above, these embodiments are shown as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and modifications thereof are included in the scope of the invention and the scope of the invention, and are also included in the scope of the invention described in the claims and the scope of equivalents thereof.

1 Motor control system 10 Feedback control unit 11 Feedforward control unit 12 Parameter identification device 120 Data acquisition unit 121 Phase diagram generation unit 122 Parameter identification unit 123 Motor side error system 124 Load side error system 2 Mechanical system 20 Motor 21 Load 22 Axis θ _t Target angle (Target rotation angle)
τ, τ'Torque θ _M Motor angle (motor angle)
ω _M Motor angular velocity (motor angular velocity)
J _M Motor side moment of inertia D _M Motor side viscosity coefficient τ _fM Motor side friction coefficient τ _fMc Motor side Coulomb friction coefficient θ _L Load angle (load angle)
ω _L load angular velocity (load angular velocity)
J _L Load-side moment of inertia D _L Load-side viscosity coefficient τ _fL Load-side friction coefficient τ _fLc Load-side Coulomb friction coefficient K _R Torsional stiffness coefficient D _R Torsional viscosity coefficient BL _Dead band width MOD 2 Inertial system model J _M0 model Motor-side inertia moment D _M0 model motor side viscosity coefficient τ _fM0 model motor side friction coefficient τ _fMc0 model motor side Coulomb friction coefficient J _L0 model load side inertia moment D _L0 model load side viscosity coefficient τ _fL0 model load side friction coefficient τ _fLc0 model load side Coulomb friction Coefficient BL ₀ Model dead zone width K _R0 Model torsional rigidity coefficient e _M Motor side error signal e _L Load side error signal δJ _M Motor side inertia moment error component δD _M Motor side viscosity coefficient error component δτ _fM Motor side friction coefficient error component δJ _L load inertia moment error component [delta] D _L load side viscosity error component .DELTA..tau _fL load side friction coefficient error component δBL dead zone width error component _{q 0M, q 1M, q 2M} motor-side internal signal _{q 0L, q 1L, q 2L} load side internal Signal q ₃ Dead band internal signal (motor side internal signal, load side internal signal)
S1, S2 Sign function F1 Dead zone characteristic function F1' Backlash displacement function F2 Dead zone characteristic change function G _M Motor side friction characteristic function G _L Load side friction characteristic function P1 Torsional angle-torque phase diagram P2 Torque-mechanical angular velocity phase diagram P3 Twist angle-mechanical angular velocity phase diagram P4 Torque-torsion velocity phase diagram P5 Twist angle-torsion velocity phase diagram P6 Load angle-torque phase diagram

Claims (12)

A parameter identification device for identifying model parameters of a two-inertia system model imitating a mechanical system in which a motor and a load are connected by a connecting member,
A data acquisition unit that acquires a torque command value for the motor, actual measurement data of the angle and angular velocity of the motor, and actual measurement data of the angle and angular velocity of the load;
A phase diagram generator that generates a plurality of phase diagrams based on the acquired torque command value, measured data of the angle and angular velocity of the motor, and measured data of the angle and angular velocity of the load,
Based on the generated plurality of phase diagrams, friction of the motor, friction of the load, rigidity of the connecting member, and a parameter identification unit for identifying a model parameter indicating the dead band width of the connecting member,
A parameter identification device comprising: The phase diagram generating unit, as the phase diagram, generates a torque-torsion velocity phase diagram showing the relationship between the torque command value and the torsion velocity of the connecting member,
The parameter identification device according to claim 1, wherein the parameter identification unit identifies a model parameter indicating friction of the motor based on a position of a break point in the torque-twist phase diagram. The phase diagram generating unit, as the phase diagram, generates a twist angle-twist velocity phase diagram showing the relationship between the twist angle of the coupling member and the twist velocity of the coupling member,
The parameter identifying unit identifies the model parameter indicating the rigidity of the connecting member and the dead band width of the connecting member based on the position of the break point in the twist angle-twist velocity phase diagram. 2. The parameter identification device described in 2. The phase diagram generating unit generates, as the phase diagram, a torque-mechanical angular velocity phase diagram showing a relationship between the torque command value and at least one of the angular velocity of the motor and the angular velocity of the load. Then
The parameter identification unit according to any one of claims 1 to 3, wherein the parameter identification unit identifies a model parameter indicating friction of the load based on a position of a break point in the torque-mechanical angular velocity phase diagram. apparatus. The phase diagram generating unit generates, as the phase diagram, a load angle-torque phase diagram showing a relationship between the load angle and the torque command value,
The parameter identification unit according to any one of claims 1 to 4, wherein the parameter identification unit identifies a model parameter indicating friction of the load based on a position of a break point in the load angle-torque phase diagram. apparatus. The phase diagram generating unit, as the phase diagram, generates a twist angle-torque phase diagram showing the relationship between the twist angle of the connecting member and the torque command value,
The parameter identification unit, based on the position of the break point in the twist angle-torque phase diagram, of the friction of the motor, the friction of the load, the rigidity of the connecting member, and the dead band width of the connecting member. The parameter identification device according to any one of claims 1 to 4, which identifies at least one of the model parameters. A motor-side error signal indicating the degree of error of the motor-side model of the two inertial system model with respect to the motor is output based on the actual measurement data of the torque command value, the motor angle, and the load angle. Motor side error system to
A load-side error system that outputs a load-side error signal that indicates the degree of error of the load-side model of the two-inertia model based on the measured data of the motor angle and the load angle. When,
Further equipped with,
The parameter identification unit,
The motor side error signal is input to identify at least a model parameter indicating the motor's moment of inertia and viscosity coefficient, and the load side error signal is input to indicate at least the load's moment of inertia and viscosity coefficient. The parameter identification device according to any one of claims 1 to 6, which identifies a model parameter. The parameter identification unit,
The torque command value acquired when the amplitude and frequency of the repetitive motion performed by the mechanical system are relatively small amplitude and low frequency, actual measurement data of the angle and angular velocity of the motor, and the angle and angular velocity of the load. Based on the actual measurement data, the model parameter indicating the friction of the motor, the friction of the load, the rigidity of the connecting member, and the dead band width of the connecting member is identified,
The torque command value acquired when the amplitude and frequency of the repetitive operation performed by the mechanical system is relatively large amplitude and high frequency, actual measurement data of the angle of the motor, and actual measurement data of the angle of the load, The parameter identification device according to claim 7, which identifies a model parameter indicating a moment of inertia and a viscosity coefficient of the motor and a model parameter indicating a moment of inertia and a viscosity coefficient of the load on the basis of the above. A parameter identification device for identifying model parameters of a two-inertia system model imitating a mechanical system in which a motor and a load are connected by a connecting member,
A data acquisition unit that acquires a torque command value for the motor, actual measurement data of the motor angle, and actual measurement data of the load angle;
On the basis of the acquired torque command value, actual measurement data of the angle of the motor, and actual measurement data of the angle of the load, a twist angle indicating the relationship between the twist angle of the connecting member and the torque command value- A phase diagram generator that generates a torque phase diagram,
Based on the generated twist angle-torque phase diagram, friction of the motor, friction of the load, rigidity of the connecting member, and a parameter identifying unit for identifying a model parameter indicating the dead band width of the connecting member. ,
A parameter identification device comprising: A parameter identification device according to any one of claims 1 to 9,
Based on the deviation of the current angle from the target angle of the load, a feedback control unit that calculates the torque command value,
Based on the model parameter identified by the parameter identification device, a feedforward control unit that calculates the torque command value,
A motor control system including. A parameter identification method for identifying a model parameter of a two-inertia system model imitating a mechanical system in which a motor and a load are connected by a connecting member,
A step of acquiring a torque command value for the motor, actual measurement data of the angle and angular velocity of the motor, and actual measurement data of the angle and angular velocity of the load;
Generating a plurality of phase diagrams based on the acquired torque command value, measured data of the angle and angular velocity of the motor, and measured data of the angle and angular velocity of the load,
Based on the generated plurality of phase diagrams, the friction of the motor, the friction of the load, the rigidity of the connecting member, and identifying a model parameter indicating the dead band width of the connecting member,
A parameter identification method having: A computer of a parameter identification device for identifying model parameters of a two-inertia system model imitating a mechanical system in which a motor and a load are connected by a connecting member,
A data acquisition unit that acquires a torque command value for the motor, actual measurement data of the angle and angular velocity of the motor, and actual measurement data of the angle and angular velocity of the load,
A phase diagram generator that generates a plurality of phase diagrams based on the acquired torque command value, measured data of the angle and angular velocity of the motor, and measured data of the angle and angular velocity of the load,
Based on the generated plurality of phase diagrams, friction of the motor, friction of the load, rigidity of the connecting member, and a parameter identification unit for identifying a model parameter indicating the dead band width of the connecting member,
Program to function as.

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