JP7066467B2 - Parameter identification device and its method and program - Google Patents

Description

The present invention relates to a parameter identification device, a method thereof, and a program.

For example, in feedforward control, a mathematical model that imitates an actual control target is created in advance, and an input command to the control system is determined by using this mathematical model. In order to improve the accuracy of feedforward control, it is important to make the response of the mathematical model as close as possible to the response of the actual controlled object. Therefore, parameter identification has been proposed in which the parameters of the mathematical model are determined based on the response of the actual controlled object (see, for example, Patent Documents 1 and 2).

For example, Patent Document 1 discloses a method for identifying a model parameter using a least squares method for a linear model.
Patent Document 2 discloses a method for identifying a model parameter of a discrete time-state-space model using a Markov parameter for a linear model of a multi-input multi-output system in a system of a discrete time-state space model.

Japanese Unexamined Patent Publication No. 2-61778 Japanese Unexamined Patent Publication No. 2008-287344

When a non-linear element is included in a mathematical model, a method of linearizing the non-linear model and identifying parameters using the linearized model is used. This is because if parameter identification is performed with the nonlinear model as it is, it takes a long time to converge and it is difficult to obtain the desired accuracy.

The present invention has been made in view of such circumstances, and is a parameter identification device and a method thereof capable of identifying parameters without linearizing a nonlinear model and improving identification accuracy. It also aims to provide a program.

The first aspect of the present invention is a simulation unit that simulates a response signal when a predetermined input signal is input to an object model that models an object, and a simulation unit when the predetermined input signal is input to the object. The response error calculated by the response error calculation unit for a response error calculation unit that calculates the response error of the response signal to the actual machine response signal and a plurality of different first evaluation functions represented as functions of the response error. The evaluation value calculation unit that calculates the output value of each of the first evaluation functions and the second evaluation function that expresses the first evaluation function as a function of each parameter error included in the object model are used. Then, it is estimated by the parameter error estimation unit that estimates the value of each parameter error so that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero, and the parameter error estimation unit. It is a parameter identification apparatus provided with a parameter correction unit which corrects the value of each parameter included in the object model by using the value of each of the parameter errors.

According to the parameter identification device, the response signal of the object model is obtained by the simulation unit, and the response error, which is the error of the response signal of the object model with respect to the response signal of the actual machine, is calculated by the response error calculation unit. Here, the response error between the actual machine and the object model can be regarded as an error caused by the motion caused by the parameter error included in the object model. Therefore, the function of response error can also be expressed as a function of parameter error included in the object model. Focusing on such a point, in the first aspect of the present invention, a first evaluation function represented as a function of response error and a second evaluation function represented as a function of parameter error are prepared. It was decided to estimate the parameter error such that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero. Then, by correcting the parameters of the object model using this parameter error, it is possible to bring each parameter of the object model closer to the true value. This makes it possible to obtain an object model with response characteristics close to those of an actual machine.
The above-mentioned "value of each parameter error such that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero" is not limited to the case where the difference becomes zero. It also includes estimating the value of each parameter error such that the difference approaches zero, for example, using the least squares method or the like.

In the parameter identification device, each of the second evaluation functions is represented as a function in which each of the parameter errors is made minute and minute changes around the parameter group of the object model are tailor-expanded, and the simulation unit is the object. The response signal when the predetermined input signal is given is calculated for each of the plurality of parameter change models in which each of the parameters of the model is slightly changed by one, and the response error calculation unit performs each of the parameters. The response error corresponding to each parameter change model is calculated from the response signal of the change model and the response signal of the actual machine, and the evaluation value calculation unit uses the response error of each parameter change model to obtain the parameter change model. The output value of each of the first evaluation functions is calculated for each, and the parameter error estimation unit outputs the output value of the first evaluation function for each parameter change model and the output of the first evaluation function of the object model. Using the value and the value, a minute coefficient corresponding to each parameter included in each of the Taylor-expanded second evaluation functions may be calculated, and each parameter error may be estimated.

According to the above parameter identification device, a plurality of parameter change models, which are object models when a plurality of parameters included in the object model are slightly changed one by one, are generated, and an input signal is input to each parameter change model. The response signal when is given is acquired, and the response error between each response signal and the actual machine response signal is obtained. By doing so, it becomes possible to individually grasp the influence of the response error caused by each parameter on the response signal. Then, using the response error obtained in this way, each parameter error is estimated so that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero.

In the parameter identification device, each of the second evaluation functions is represented by, for example, the following equation. In the following equation, J1 to Jr are a plurality of the second evaluation functions, δP1 to δPn are the parameter errors, and the matrix ΔM is a fine coefficient matrix.

Further, in the above equation, each element of the matrix ΔM may be expressed by the following equation.

When the parameter identification device gives the predetermined input signal to the model update unit that updates the object model by using each of the parameters corrected by the parameter correction unit and the updated object model. The response signal may be further provided with a determination unit for determining whether or not the evaluation value based on the response error between the response signal and the actual machine response signal is within the preset allowable range. When the determination unit determines that the evaluation value is not within the permissible range, the parameter error may be calculated again using the updated object model.

According to the parameter identification device, the parameter error is estimated repeatedly until the evaluation value based on the response error of the response signal of the object model to the response signal of the actual machine is within a predetermined allowable range, so that the response of the object model can be obtained. It is possible to get closer to the response of the actual machine.

The second aspect of the present invention is a simulation step of simulating a response signal when a computer inputs a predetermined input signal to an object model in which an object is modeled, and inputting the predetermined input signal to the object. In the response error calculation step, a response error calculation step of calculating the response error of the response signal with respect to the actual machine response signal and a plurality of different first evaluation functions expressed as a function of the response error were calculated. An evaluation value calculation step for calculating the output value of each of the first evaluation functions by giving the response error, and a second evaluation in which the first evaluation function is represented as a function of each parameter error included in the object model. A parameter error estimation process for estimating the value of each parameter error such that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero using a function, and the parameter error estimation. It is a parameter identification method which performs a parameter correction step which corrects the value of each parameter included in the object model using the value of each said parameter error estimated in the step.

A third aspect of the present invention is a simulation process that simulates a response signal when a predetermined input signal is input to an object model that models an object, and a simulation process when the predetermined input signal is input to the object. The response error calculated in the response error calculation process for a response error calculation process for calculating the response error of the response signal to the actual machine response signal and a plurality of different first evaluation functions represented as functions of the response error. The evaluation value calculation process for calculating the output value of each of the first evaluation functions and the second evaluation function of expressing the first evaluation function as a function of each parameter error included in the object model are used. The parameter error estimation process for estimating the value of each parameter error such that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero, and the estimation in the parameter error estimation process. It is a parameter identification program for causing a computer to execute a parameter correction process for correcting the value of each parameter included in the object model by using the value of each of the parameter errors.

According to the present invention, it is possible to identify parameters without linearizing a nonlinear model, and it is possible to improve the identification accuracy.

It is a figure which showed the structure which obtains the response error from the response signal of the actual machine of an object, and the response signal of an object model. It is a schematic block diagram which showed an example of the hardware configuration of the parameter identification apparatus which concerns on one Embodiment of this invention. It is a functional block diagram which showed an example of the function which the parameter identification apparatus which concerns on one Embodiment of this invention has. It is a flowchart which showed an example of the processing procedure of the parameter identification apparatus which concerns on one Embodiment of this invention. It is a flowchart which showed an example of the processing procedure of the parameter identification apparatus which concerns on one Embodiment of this invention.

Hereinafter, a parameter identification apparatus according to the present invention, a method thereof, and an embodiment of a program will be described with reference to the drawings.

First, the concept of parameter identification according to an embodiment of the present invention will be described with reference to the drawings.
First, a mathematical model of the object for which the parameters are to be identified (hereinafter referred to as "object model") is created. The object model may be a model including non-linear elements or a model having only linear elements not including non-linear elements. Further, the object model may be, for example, a model of only a controlled object to be controlled, or may be a model including a control system. Examples of objects include mechanical systems such as robots and moving objects such as underwater vehicles, but the application of the present invention is not limited to such objects, and structures that require parameter identification are used. It can be widely applied.

First, as shown in FIG. 1, for convenience, the characteristics of the actual machine of the object 10 are shown by the equation (1).

dX / dt = f (X, P, U) (1)

In the equation (1), f is a function of the object 10, X is a state vector (dimension k) of the object 10, P is a parameter group (dimension n) of the object 10, and U is an input (dimension q). There is.

The mathematical model of the object 10 (object model) is expressed by the following equation (2).

dXm / dt = fm (Xm, Pm, Um) (2)

In equation (2), fm is a model function, Xm is a model state vector, Pm is a model parameter group, and Um is a model input.
Here, it is assumed that the object model has the same structure of the equation and has an error only in the parameters. That is, in this embodiment, the parameters are identified under this condition.

First, when the object model has a parameter error with respect to the object 10, the actual machine response signal X when the input signal U is given to the actual machine of the object 10 and the input signal U when the input signal U is given to the object model. Since it is different from the response signal Xm, if the response error is ΔX and the parameter error is ΔP, it is expressed as the following equations (3) and (4).

ΔX ＝ X－Xm (3)
ΔP = P－Pm (4)

Next, the first evaluation function J regarding the response error is defined. The first evaluation function J expressed as a function of the response error is expressed by the following equation (5).

In the equation (5), T1 and T2 are time intervals for evaluating the response error by integration, and can be arbitrarily set. Further, g is a function of the first evaluation function. The first evaluation function J becomes zero in the absence of the parameter error described above. As the first evaluation function J, a plurality of different functions are prepared, and the number thereof is r (J1, J2, J3, ... Jr).

On the other hand, the response error ΔX between the actual machine of the object 10 and the object model can be regarded as an error caused by the motion caused by the parameter error included in the object model. Then, the first evaluation function J expressed as a function of the response error can also be expressed as a function of the parameter error of the object model, and can be expressed as the following equation (6).

For example, the output value when the response error ΔX is substituted into the first evaluation functions J1, J2, J3, ... Jr can be expressed as the sum of the motions generated by each parameter error δP1 to δPn. Then, if each parameter error δPi (i = 1 to n) is set to a minute value and the minute change around the parameter group Pm of the above-mentioned object model is expanded by Taylor to obtain an equation up to the first order, the first evaluation functions J1 to Jr. Can be expressed by the following equation (7). In this embodiment, the evaluation function represented by the following equation (7) is referred to as a "second evaluation function" for convenience.
This second evaluation function is expressed by grasping the first evaluation function from the viewpoint of parameter error, and is expressed as a function of each parameter error included in the object model.
Since the first evaluation function and the second evaluation function are expressed from different viewpoints, it can be said that the output value of the first evaluation function and the output value of the second evaluation function are equivalent. That is, the value of each parameter error may be estimated so that the difference between the output value of the first evaluation function and the output value of the second evaluation function becomes zero.

In the equation (7), the fine coefficient ∂J / Pi cannot be solved as it is because the function is unknown. Therefore, if ∂J / Pi is approximated to ΔJ / ΔPi, the above equation (7) is the following equation (8). It can be expressed by the determinant shown in.

In the above equation (8), each element of the matrix ΔM, in other words, each minute coefficient (ΔJ1 / ΔP1 to ΔJr / ΔPn) in the Taylor expansion, has one parameter Pj (j = 1 to n) of the object model. It can be obtained numerically from the change in the response signal of the object model when a small change (Pj + ΔPj) is made.
For example, a response when an object model (hereinafter referred to as "parameter change model") is generated when the parameters of the object model are individually and minutely changed, and an input signal U is given to the parameter change model. Each signal is obtained by simulation. Then, the response error corresponding to each parameter change model is calculated from each response signal of each parameter change model and the actual machine response signal, and the output value of each first evaluation function is calculated for each parameter change model using the calculated response error. Using the output value of the first evaluation function for each of these parameter change models and the output value of the first evaluation function of the object model, the minute coefficient corresponding to each parameter included in the second evaluation function ( ΔJ1 / ΔP1 to ΔJr / ΔPn) is calculated.
For example, the fine coefficient in the above equation (8) is expressed by the following equation (9).

In this way, when the fine coefficients of each parameter in the equation (8), in other words, each element of the matrix ΔM is calculated, the only unknown parameter in the equation (8) is the parameter error δP1 to δPn. Therefore, for example, the unknown parameter errors δP1 to δPn can be obtained by the least squares method using the evaluation function given by the following equation (10).

δPV ＝ (ΔM ^T ΔM) ^-1 JV (10)
δPV ＝ (δP1, δP2, ・ ・ ・ δPn) ^T
JV = (J1, J2, ... Jr) ^T

Further, when the dimension n of the parameter Pm and the number r of the evaluation function are the same (n = r), a square matrix is obtained. It is possible to calculate unknown parameter errors δP1 to δPn using the matrix ΔM ^-1 .

δPV ＝ ΔM ^-1・ JV (11)

In this way, when the parameter errors δP1 to δPn for each parameter are estimated, each parameter used in the object model is corrected by using these estimated parameter errors, and each parameter is updated. For example, the corrected parameter is a value obtained by subtracting the value of each parameter error from the initial value of each parameter set in the object model. At this time, in consideration of convergence, the value of each parameter may be updated by subtracting the value obtained by multiplying the parameter error by the adjustment coefficient k (k <1) from the initial value of each parameter.

Next, a specific device configuration for realizing the parameter identification according to the present embodiment described above will be described.

FIG. 2 is a schematic configuration diagram showing an example of the hardware configuration of the parameter identification device 1 according to the embodiment of the present invention. As shown in FIG. 1, the parameter identification device 1 has a computer (computer system), and is, for example, a CPU 11, an auxiliary storage device 12 for storing a program executed by the CPU 11, data referenced by the program, and the like. A main storage device 13 that functions as a work area when executing each program, a communication interface 14 for connecting to a network, an input unit 15 consisting of a keyboard, a mouse, etc., and a display unit 16 consisting of a liquid crystal display device for displaying data, etc. It is equipped with. Each of these parts is connected via, for example, a bus 18. Examples of the auxiliary storage device 12 include a magnetic disk, a magneto-optical disk, a semiconductor memory, and the like.

As an example, a series of processes for realizing various functions described later are stored in the auxiliary storage device 12 in the form of a program (for example, a design support program), and the CPU 11 reads this program into the main storage device 13. , Various functions are realized by executing information processing and arithmetic processing. The program is distributed via a form pre-installed in the auxiliary storage device 12, a form provided in a state of being stored in a storage medium readable by another computer, or a wired or wireless communication means. The form and the like may be applied. The computer-readable storage medium is a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like.

FIG. 3 is a functional block diagram showing an example of the functions of the parameter identification device 1 according to the present embodiment. As shown in FIG. 3, the parameter identification device 1 includes a storage unit 20, a simulation unit 21, a response error calculation unit 22, an evaluation value calculation unit 23, a parameter error estimation unit 24, and a parameter correction unit 25. The model update unit 26 and the determination unit 27 are provided as the main configurations.

The storage unit 20 stores various arithmetic expressions, data, and the like necessary for parameter identification. For example, the storage unit 20 is used to estimate an object model that models an object, initial values of each parameter included in the object model, various arithmetic expressions described above, for example, a plurality of evaluation functions, and parameter errors. Various arithmetic expressions (for example, the above (3), (5), (6), (8) to (11) equations, etc.), and various data required for parameter identification using these arithmetic expressions. Etc. are stored. The storage unit 20 is also used as a storage area for temporarily storing data generated in the process when executing the parameter identification process.

The simulation unit 21 obtains a response signal Xm when a predetermined input signal U is input to the object model that models the object 10 by simulation. Further, the simulation unit 21 calculates a response signal when a predetermined input signal U is given to a plurality of parameter change models in which the values of each parameter of the object model are slightly changed one by one.

The response error calculation unit 22 calculates the response error ΔX of the response signal Xm of the object model to the actual machine response signal X, which is the response signal when the predetermined input signal U is input to the actual machine of the object 10. Further, the response error calculation unit 22 calculates the response error corresponding to each parameter change model from the response signal of each parameter change model and the actual machine response signal X.

The evaluation value calculation unit 23 substitutes the response error ΔX calculated by the response error calculation unit 22 into a plurality of different first evaluation functions J1 to Jr stored in the storage unit 20, so that each first evaluation function J1 Obtain the output value of ~ Jr. Further, the evaluation value calculation unit 23 substitutes the response error of each parameter change model calculated by the response error calculation unit 22 into the first evaluation functions J1 to Jr, so that the first evaluation value calculation unit 23 is used for each parameter change model. Calculate the output values of the evaluation functions J1 to Jr.

Here, the first evaluation function is given as a function of the response error as described above. As an example of the first evaluation function, the following equations (12) and (13) can be mentioned. The first evaluation function is given, for example, as a function that integrates the response error in a predetermined time interval as shown in the equation (12). Further, the first evaluation function is given as a function that integrates the square of the response error in a predetermined time interval, for example, as shown in the equation (13). By using equation (12), it is possible to grasp the increasing / decreasing direction of whether the response error is increasing or decreasing in the process of converging the parameter error, and by using equation (13), the response error can be determined. It is possible to grasp the fluctuation of the absolute value.

The parameter error estimation unit 24 acquires the second evaluation function from the storage unit 20 and estimates the value of each parameter error so that the difference between the output value of the second evaluation function and the output value of the first evaluation function becomes zero. do. Specifically, each parameter included in each second evaluation function is used for the second evaluation function by using the output value of the first evaluation function for each parameter change model and the output value of the first evaluation function of the object model. Calculate the merit function corresponding to, and estimate each parameter error.

The parameter correction unit 25 corrects the value of each parameter included in the object model by using the value of each parameter error estimated by the parameter error estimation unit 24.
The model update unit 26 updates the object model by using each new parameter corrected by the parameter correction unit 25.
The determination unit 27 obtains a response signal when a predetermined input signal is given to the updated object model, and an evaluation value based on a response error between the response signal and the actual machine response signal which is the response of the actual machine of the object 10 is obtained. It is determined whether or not it is within the preset allowable range. For example, the output values of the first evaluation functions J1 to Jr calculated by using the response error between the response signal of the updated object model and the response signal of the actual machine are within the predetermined allowable range set for each evaluation function. Determine if it exists.
As a result, for example, if there is even one evaluation function whose output value of the evaluation function is not within the permissible range, it is determined that the parameter error included in the current parameter is large, and the parameter identification device 1 determines that the parameter error is large. Parameter identification is performed again using the updated target model. It should be noted that this determination criterion can be appropriately set according to the accuracy required for the object model.

Next, the parameter identification method realized by the parameter identification device 1 according to the present embodiment will be described with reference to FIGS. 4 to 5. 4 and 5 are flowcharts showing an example of the procedure of the parameter identification method according to the present embodiment. The parameter identification method described later is realized, for example, by reading the parameter identification program stored in the auxiliary storage device 12 into the main storage device 13 and executing information processing / arithmetic processing.

In the following description, for convenience of explanation, a case where two parameters to be identified are included in the parameter group of the object model is given as a specific example, and these two parameters Pm = (P1m, P2m) are identified. The case will be described. Further, as the first evaluation function used, it is assumed that the two first evaluation functions J1 and J2 represented by the above-mentioned equations (13) and (14) are used. Further, it is assumed that P1m (0) and P2m (0) are set as the initial values of the parameters Pm in the object model.
The procedure of the parameter identification method shown in FIGS. 4 and 5 is an example, and unnecessary steps are deleted, new steps are added, or the processing order is changed within a range not deviating from the gist of the present invention. You may do it. Further, a plurality of processes may be performed in parallel.

First, a response signal Xm_0 (t) is obtained by giving a predetermined input signal U to the object model (SA1).
Next, the response error ΔX_0 (t), which is the difference between the response signal X_0 (t) when the input signal U is given to the actual machine of the object and the response signal Xm_0 (t), is calculated (SA2).
Subsequently, the output values J1_0 and J2_0 when the response error ΔX_0 (t) is given to the first evaluation functions J1 and J2 are calculated (SA3).
The response error ΔX_0 (t), output values J1_0, and J2_0 are temporarily stored in the storage unit 20.

Subsequently, a parameter change model, which is an object model in which only the parameter P1m (0) of the target model is slightly changed by ΔP1, is created, and a response signal Xm_1 when a predetermined input signal U is given to this parameter change model. (T) is obtained by simulation (SA4).

Subsequently, the response error ΔX_1 (t) between the response signal Xm_1 (t) and the actual machine response signal X is calculated (SA5), and further, the calculated response error ΔX_1 (t) is used to generate the first evaluation functions J1 and J2, respectively. Output values J1_1 and J2_1 of are calculated (SA6).
The response error ΔX_1 (t) and the output values J1_1 and J2_1 are temporarily stored in the storage unit 20.

Next, a parameter change model is created in which only the parameter P2m (0) of the target model is slightly changed by ΔP2, and the response signal Xm_2 (t) when a predetermined input signal U is given to this parameter change model is simulated. Obtained by (SA7).

Subsequently, the response error ΔX_2 (t) between the response signal Xm_2 (t) and the actual machine response signal X is calculated (SA8), and further, the calculated response error ΔX_2 (t) is used to generate the first evaluation functions J1 and J2, respectively. Output values J1_2 and J2_2 of are calculated (SA9).
The response error ΔX_2 (t), output values J1_2, and J2_2 are temporarily stored in the storage unit 20.

Subsequently, using the above calculation results ΔX_0 (t), ΔX_1 (t), ΔX_2 (t), J1_0, J2_0, J1_1, J2_1, J1-2, J2_1 and minute change amounts ΔP1 and ΔP2 stored in the storage unit 20, Estimate the parameter error (SA10).

Specifically, each element of the matrix ΔM of the above-mentioned equation (8) is calculated, and the parameter errors δP1 and δP2 are estimated using the inverse matrix ΔM ^-1 .
Here, each element of the matrix ΔM is calculated by the following equation.

ΔM [1,1] ＝ ΔJ1 / ΔP1 ＝ [J1_1-J1_0] / ΔP1
ΔM [1,2] ＝ ΔJ1 / ΔP2 ＝ [J1_2-J1_0] / ΔP2
ΔM [2,1] ＝ ΔJ2 / ΔP1 ＝ [J2_1-J2_0] / ΔP1
ΔM [2,2] ＝ ΔJ2 / ΔP2 ＝ [J2_2-J2_0] / ΔP2

Then, using the inverse matrix of the above matrix ΔM, the parameter errors δP1 and δP2, which are unknown parameters, are calculated from the following equations.

δPV ＝ ΔM ^-1・ JV
Here, δPV = (δP1, δP2) ^T , JV = [J1_0, J2_0].

When the parameter errors δP1 and δP2 are calculated in this way, the initial values P1m (0) and P2m (0) of each parameter are corrected by using the parameter errors δP1 and δP2 (SA11 in FIG. 5). The corrected values P1e and P2e of each parameter are expressed by the following equations. Here, k is an adjustment coefficient in consideration of convergence.

P1e ＝ P1m (0) -k ・ δP1
P2e ＝ P2m (0) -k ・ δP2

Subsequently, an object model using the corrected parameters P1e and P2e is generated, and the object model is updated (SA12). Subsequently, a response signal when a predetermined input signal U is given to the updated object model is acquired by simulation (SA13). Then, the response error between the response signal and the actual machine response signal is calculated (SA14), and the output value of the evaluation function using the calculated response error is calculated (SA15).
Subsequently, it is determined whether or not the output value is within the predetermined allowable range (SA16), and if it is within the predetermined allowable range (SA16: YES), the parameters of each parameter used in the current object model are used. The value is fixed (SA17), and this process ends.

On the other hand, when the output value is out of the predetermined allowable range (SA16: NO), the updated target model, that is, the parameters P1e and P2e as initial values (SA18), returns to step SA1 and the updated target. The processing after step SA1 is performed using the model. Then, in step SA16, the above processing is repeated until it is determined that the output value is within a predetermined allowable range.

By repeating the processes of steps SA1 to SA18 a plurality of times, it is possible to discriminate between a parameter having a high sensitivity and a parameter having a low sensitivity to the response signal of the object model. For example, a parameter with low sensitivity does not have much effect on the response signal even if the value of the parameter is slightly changed. For such parameters, even if the parameter error is included, the influence of the parameter error on the response signal can be regarded as very small. Therefore, such a low-sensitivity parameter error may be regarded as zero. .. In this way, when a parameter with low sensitivity is discriminated, the number of unknowns can be reduced by setting the parameter error to zero. This makes it possible to reduce the rank of the matrix. As a result, the operational processing load of parameter identification can be reduced, and the time can be shortened.

As described above, according to the parameter identification device 1 according to the present embodiment, the method thereof, and the program, the response error between the actual machine and the object model is caused by the motion caused by the parameter error included in the object model. The parameter error is estimated by regarding it as an error and expressing the function of the response error as a function of the parameter error included in the object model. Then, by correcting the parameters of the object model using the estimated parameter error, it is possible to bring each parameter of the object model closer to the true value. This makes it possible to obtain an object model with response characteristics close to those of an actual machine. Further, according to the present embodiment, the nonlinear model can be used as it is for parameter identification.

Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. Various changes or improvements can be made to the above embodiments without departing from the gist of the invention, and the modified or improved embodiments are also included in the technical scope of the present invention. Moreover, you may combine the said embodiment as appropriate.

1: Parameter identification device 10: Object 20: Storage unit 21: Simulation unit 22: Response error calculation unit 23: Evaluation value calculation unit 24: Parameter error estimation unit 25: Parameter correction unit 26: Model update unit 27: Judgment unit

Claims (6)

A simulation unit that simulates a response signal when a predetermined input signal is input to an object model that models an object, and a simulation unit.
A response error calculation unit that calculates the response error of the response signal to the actual machine response signal when the predetermined input signal is input to the object, and a response error calculation unit.
An evaluation value for calculating the output value of each of the first evaluation functions by giving the response error calculated by the response error calculation unit to a plurality of different first evaluation functions represented as functions of the response error. Calculation unit and
Using the second evaluation function that expresses the first evaluation function as a function of each parameter error included in the object model, the difference between the output value of the first evaluation function and the output value of the second evaluation function is A parameter error estimator that estimates the value of each of the parameter errors so that it becomes zero,
A parameter identification device including a parameter correction unit that corrects the value of each parameter included in the object model by using the value of each parameter error estimated by the parameter error estimation unit. Each of the second evaluation functions is expressed as a function in which each of the parameter errors is made minute and the minute change around the parameter group of the object model is Taylor-expanded.
The simulation unit calculates a response signal when a predetermined input signal is given to a plurality of parameter change models in which each parameter of the object model is slightly changed by one.
The response error calculation unit calculates the response error corresponding to each parameter change model from the response signal of each parameter change model and the actual machine response signal.
The evaluation value calculation unit calculates the output value of each first evaluation function for each parameter change model by using the response error of each parameter change model.
The parameter error estimation unit uses the output value of the first evaluation function for each parameter change model and the output value of the first evaluation function of the object model to each of the second Taylor-expanded units. The parameter identification device according to claim 1, wherein a minute coefficient corresponding to each parameter included in the evaluation function is calculated and each parameter error is estimated. Each of the second evaluation functions is expressed by the following equation (1).

In the above equation (1), J1 to Jr are a plurality of the second evaluation functions, δP1 to δPn are the respective parameter errors, the matrix ΔM is a fine coefficient matrix, and each element is represented by the above equation (2). The parameter identification device according to claim 2. A model update unit that updates the object model by using each of the parameters corrected by the parameter correction unit, and a model update unit.
The response signal when the predetermined input signal is given to the updated object model is obtained, and the evaluation value based on the response error between the response signal and the actual machine response signal is within a preset allowable range. A judgment unit that determines whether or not it is
The parameter identification according to any one of claims 1 to 3, wherein when the determination unit determines that the evaluation value is not within the permissible range, the parameter error is calculated again using the updated object model. Device. The computer
A simulation process that simulates a response signal when a predetermined input signal is input to an object model that models an object, and a simulation process.
A response error calculation step for calculating the response error of the response signal to the actual machine response signal when the predetermined input signal is input to the object, and a response error calculation step.
An evaluation value for calculating the output value of each of the first evaluation functions by giving the response error calculated in the response error calculation step to a plurality of different first evaluation functions represented as functions of the response error. Calculation process and
Using the second evaluation function that expresses the first evaluation function as a function of each parameter error included in the object model, the difference between the output value of the first evaluation function and the output value of the second evaluation function is A parameter error estimation process for estimating the value of each of the parameter errors so as to be zero, and
A parameter identification method for executing a parameter correction step of correcting the value of each parameter included in the object model by using the value of each parameter error estimated in the parameter error estimation step. Simulation processing that simulates the response signal when a predetermined input signal is input to the object model that models the object, and
Response error calculation processing for calculating the response error of the response signal to the actual machine response signal when the predetermined input signal is input to the object, and
An evaluation value for calculating the output value of each of the first evaluation functions by giving the response error calculated by the response error calculation process to a plurality of different first evaluation functions represented as functions of the response error. Arithmetic processing and
Using the second evaluation function that expresses the first evaluation function as a function of each parameter error included in the object model, the difference between the output value of the first evaluation function and the output value of the second evaluation function is Parameter error estimation processing that estimates the value of each of the parameter errors so that it becomes zero, and
A parameter identification program for causing a computer to perform a parameter correction process for correcting the value of each parameter included in the object model by using the value of each parameter error estimated in the parameter error estimation process.

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