CN110989357B - Identification and control method and system of a complex electromechanical system - Google Patents
Identification and control method and system of a complex electromechanical system Download PDFInfo
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Abstract
本发明公开了一种复杂机电系统的辨识控制方法和系统，应用于六自由度调整转台，方法包括：将六自由度调整转台划分为机械部分和电气部分，获取机械部分的机械模型，并获取电气部分的类型以确定其电气部分模型结构；对机械模型进行谐响应分析仿真，获取包含若干个所述传递函数模型的传递函数模型集合；在传递函数模型集合中获取优选的传递函数模型，并以优选的传递函数模型建立机械部分的导出模型结构；以电气部分模型结构和导出模型结构得到整体模型，根据整体模型设计电机控制器的控制参数，并控制电机控制器依据控制参数控制转台的工作。本发明针对复杂机械结构，其分析结果更加准确可靠。
The invention discloses an identification control method and system for a complex electromechanical system, which is applied to a six-degree-of-freedom adjustment turntable. The method includes: dividing the six-degree-of-freedom adjustment turntable into a mechanical part and an electrical part, obtaining a mechanical model of the mechanical part, and obtaining The type of the electrical part determines the model structure of its electrical part; perform harmonic response analysis and simulation on the mechanical model, and obtain a transfer function model set containing several of the transfer function models; obtain the preferred transfer function model in the transfer function model set, and The derived model structure of the mechanical part is established with the preferred transfer function model; the overall model is obtained from the electrical part model structure and the derived model structure, the control parameters of the motor controller are designed according to the overall model, and the motor controller is controlled to control the work of the turntable according to the control parameters . The present invention is aimed at complex mechanical structures, and its analysis results are more accurate and reliable.
Description
Technical Field
The invention relates to the technical field of system identification, in particular to an identification control method and system for a complex electromechanical system.
Background
In the motor control process aiming at the six-degree-of-freedom rotary table, one of the core links of the existing system identification technology is the determination of a model structure, and the prior knowledge and the mathematical and physical analysis of the whole system are relied on. During the analysis process, some interference factors which have small influence are generally directly ignored. The model structure determination method has good applicability to low-order, simple pure mechanical and electrical systems, but for complex electromechanical systems, due to high control precision requirement, interference factors with little influence need to be considered, due to less priori knowledge, the analysis difficulty is high, the system model structure is difficult to determine effectively, and difficulty is caused for subsequent system identification and controller design work.
In the prior art, the determination method of the model structure includes methods according to residual variance, AIC criterion, determinant ratio, Hankel matrix and the like.
However, in the actual process of system identification of a complex electromechanical system and subsequent controller design, the requirement on control accuracy is high, and meanwhile, in the actual control of the system, the noise influence is relatively large, so that in the experimental process, the accuracy of a transfer function model established based on a model structure determined by the series of traditional methods is low, the control effect of the controller based on model design is poor, and finally, the control on a six-degree-of-freedom turntable cannot reach the predicted accuracy degree.
In summary, how to make the controller of the turntable have good control effect and high precision is a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of this, the present invention provides an identification control method and system for a complex electromechanical system, which are used to improve the control accuracy of the complex electromechanical system and have a good control effect.
In order to achieve the above purpose, the invention provides the following technical scheme:
the identification control method of the complex electromechanical system is applied to a six-degree-of-freedom adjusting rotary table, wherein the six-degree-of-freedom adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, wherein the voice coil motor and the motor controller are arranged at the lower part of the rotary table; the method comprises the following steps:
dividing a six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part, acquiring a mechanical model of the mechanical part, and acquiring the type of the electrical part to determine the model structure of the electrical part;
carrying out harmonic response analysis simulation on the mechanical model to obtain single group of frequency response data; obtaining a transfer function model set comprising a plurality of transfer function models according to the single group of frequency response data;
acquiring a preferred transfer function model from the transfer function model set, and establishing a derived model structure of the mechanical part by using the preferred transfer function model;
and obtaining an integral model structure by using the electric part model structure and the derived model structure, performing parameter estimation by using the integral model structure to obtain an integral model, designing control parameters of the motor controller according to the integral model, and controlling the motor controller to control the work of the rotary table according to the control parameters.
Preferably, obtaining a set of transfer function models including a number of the transfer function models from the single set of frequency response data comprises:
taking the single group of frequency response data as input/output data, and performing system identification by using an auxiliary variable method to obtain the transfer function model set G divided by orders and zero numbers;
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
wherein np represents the order, nz represents the number of zero points; nz < np; np and nz are both integers.
Preferably, obtaining a preferred transfer function model in the set of transfer function models includes:
and acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model, and selecting an optimal transfer function model according to the similarity of the amplitude-frequency characteristic curve and the normalized root mean square error, wherein the transfer function model with high similarity is the optimal transfer function model.
Preferably, obtaining an overall model structure from the derived model structure and the electrical part model structure, and performing parameter estimation with the overall model structure to obtain an overall model, includes:
obtaining the preferred transfer function model structure Gm(s) obtaining the electrical part model structure Ge(s)；
Obtaining an overall model structure G(s);
wherein G(s) ═ Gm(s)·Ge(s)，Gm(s) is the structure of the derived model, Ge(s) is the electrical part model structure;
carrying out parameter estimation on the whole model structure G(s) to obtain a parameter-containing whole model G'(s); for controlling said motor controller by said integral model G'(s) containing parameters.
Preferably, designing a control parameter of the motor controller according to the integral model, and controlling the motor controller to control the work of the turntable according to the control parameter includes:
and setting the output current of the motor controller according to the integral model, and controlling the motor controller to output the output current to the voice coil motor so as to control the work of the rotary table.
The identification control system of the complex electromechanical system is applied to a six-degree-of-freedom adjusting rotary table, and the six-degree-of-freedom adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, wherein the voice coil motor and the motor controller are arranged at the lower part of the rotary table; the identification control system comprises:
the classification acquisition module is used for dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part, acquiring a mechanical model of the mechanical part and acquiring the type of the electrical part to determine the model structure of the electrical part;
the mechanical transfer function model acquisition module is used for carrying out harmonic response analysis simulation on the mechanical model to obtain single group of frequency response data; obtaining a transfer function model set comprising a plurality of transfer function models according to the single group of frequency response data; the mechanical transfer function model acquisition module is connected with the classification acquisition module;
the optimization module is used for acquiring an optimized transfer function model from the transfer function model set and establishing a derived model structure of the mechanical part by using the optimized transfer function model; the optimal module is connected with the mechanical transfer function model acquisition module;
the fitting control module is used for obtaining an integral model structure by using the electric part model structure and the derived model structure, performing parameter estimation by using the integral model structure to obtain an integral model, obtaining control parameters of the motor controller according to the integral model, and controlling the motor controller to control the work of the rotary table according to the control parameters; the fitting control module is connected with the optimal selection module, the classification acquisition module and the motor controller.
Preferably, the mechanical transfer function model obtaining module includes:
the first acquisition unit is used for acquiring the single group of frequency response data and is connected with the classification acquisition module;
the system identification unit is connected with the acquisition unit and used for carrying out system identification by using the single group of frequency response data as input/output data and utilizing an auxiliary variable method to acquire the transfer function model set G divided by orders and zero numbers;
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
wherein np represents the order, nz represents the number of zero points; nz < np; np and nz are both integers.
Preferably, the preferred module comprises:
an amplitude-frequency characteristic curve obtaining unit, configured to obtain an amplitude-frequency characteristic curve of each transfer function model;
a normalized root mean square error acquisition unit for obtaining normalized root mean square errors of the transfer function models;
the comparison unit is used for determining the similarity between the amplitude-frequency characteristic curve of the transfer function model and the normalized root mean square error, and taking the transfer function model with the maximum similarity as the preferred transfer function model; and the comparison unit is connected with the amplitude-frequency characteristic curve acquisition unit and the standardized root-mean-square error acquisition unit.
Preferably, the fitting control module comprises:
a fitting unit for obtaining the derived model structure Gm(s) obtaining the electrical part model structure Ge(s) and obtaining the overall model structure g(s); wherein G(s) ═ Gm(s)·Ge(s)，Gm(s) is the structure of the derived model, Ge(s) is the electrical part model structure; carrying out parameter estimation on the whole model structure G(s) to obtain a whole model G'(s) containing parameters;
and the control unit is connected with the fitting unit and used for acquiring the control parameters of the motor controller according to the integral model G'(s) and controlling the motor controller to control the work of the rotary table according to the control parameters.
Preferably, the control unit includes:
and the current control unit is connected with the fitting unit, controls the output current of the motor controller according to the integral model G'(s), and controls the motor controller to output the output current to the voice coil motor so as to control the work of the rotary table.
According to the method, a complex electromechanical system is divided into a mechanical part and an electrical part, a model structure of the electrical part is obtained in a theoretical mode, a corresponding transfer function model is obtained through a rotary table actual model aiming at the mechanical part with a complex structure, a derived model structure of the mechanical part is further obtained, and finally the model structure of the electrical part is integrated to obtain an integral model structure.
The invention carries out modeling and analysis aiming at the complex mechanical structure, can effectively avoid the inaccuracy of pure theoretical analysis caused by less prior knowledge, and in the actual analysis process, the actual data is used as the input/output of system identification, so that the analysis result is more accurate and reliable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flow chart of a method for identifying and controlling a complex electromechanical system according to the present invention;
FIG. 2 is a schematic flow chart of an identification control method according to the present invention;
FIG. 3 is a front view of a motor control system for a six degree-of-freedom turret;
FIG. 4 is a top view of a motor control system for a six degree-of-freedom turret;
fig. 5 is a left side view of a motor control system of a six degree-of-freedom turret.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The core of the invention is to provide the identification control method and the identification control system for the complex electromechanical system, which are used for improving the control precision of the complex electromechanical system and have good control effect.
Referring to fig. 1 to 5, fig. 1 is a flowchart illustrating an identification control method for a complex electromechanical system according to the present invention; FIG. 2 is a schematic flow chart of an identification control method according to the present invention; fig. 3-5 are front, top, and left views, respectively, of a motor control system for a six degree-of-freedom turret.
The identification control method of the complex electromechanical system is applied to a six-degree-of-freedom adjusting rotary table, and the six-degree-of-freedom adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, wherein the voice coil motor and the motor controller are arranged at the lower part of the rotary table; the method comprises the following steps:
step S1, dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part, acquiring a mechanical model of the mechanical part, and acquiring the type of the electrical part to determine the model structure of the electrical part;
step S2, carrying out harmonic response analysis simulation on the mechanical model to obtain single group of frequency response data; obtaining a transfer function model set comprising a plurality of transfer function models according to the single group of frequency response data;
step S3, obtaining an optimal transfer function model from the transfer function model set, and establishing a derived model structure of the mechanical part by using the optimal transfer function model;
and step S4, obtaining an integral model by the electric part model structure and the derived model structure, designing control parameters of the motor controller according to the integral model, and controlling the motor controller to control the work of the rotary table according to the control parameters.
Referring to fig. 3 to 5, a schematic diagram of a six-degree-of-freedom adjustable turntable is shown, wherein a support adjusting leg is disposed below the turntable 1, and the turntable 1 is supported and adjusted in 6 directions, and the support forces are respectively F in fig. 41、F2、F3、F4、F5And F6The support adjusting support legs can be adjusted respectively to change the space state of the rotary table, the support adjusting support legs are correspondingly connected with the voice coil motors respectively, the rotary table and other connecting structures are mechanical parts of the six-freedom-degree adjusting rotary table, and the voice coil motors are electrical parts of the six-freedom-degree adjusting rotary table. The division of the whole into the mechanical part and the electrical part in step S1 does not mean the separation of the entity, but means the division of the entity into the mechanical part and the electrical partAnalysis and investigation were performed separately.
The mechanical model refers to a three-dimensional structural model of a mechanical part, and modeling analysis can be performed through software. The electrical part model structure refers to an electrical part model structure corresponding to the type of the electrical part, which is determined according to the type of the electrical part and the prior knowledge and theoretical analysis, and specifically may be a transfer function model structure of the electrical part mentioned later, that is, a transfer function model without parameters.
In step S2, a harmonic response analysis simulation is performed on the modeled mechanical model, the harmonic response analysis simulation is used to determine the steady-state response of the linear structure when subjected to a load that varies sinusoidally with time, and aims to calculate the response value versus frequency curves of the structure at several frequencies. Harmonic response analysis simulations are applicable to the analytical version of the turntable in this application.
The application provides a scheme based on ANSYS modeling analysis and harmonic response analysis.
It should be noted that the harmonic response analysis simulation includes: the input harmonic response analyzes the material, density and frequency band of interest of the simulation object.
Alternatively, the frequency setting may be 1-500Hz in this application, thereby obtaining a single set of frequency response data.
The single group of frequency response data obtained above is used for system identification, specifically, an auxiliary variable method is adopted, the single group of frequency response data is used as input/output data, and a corresponding transfer function is obtained. In the process, the system identification can be carried out by an auxiliary variable method in the prior art.
In step S3, an optimal transfer function model is obtained by performing optimization selection on the set of transfer function models, and a derived model structure is obtained from the optimal transfer function model and used for obtaining an overall model by combining with the electrical model. The derivation model obtained by the optimized transfer function model is substantially a de-parameterization process, namely, parameters of the original optimized transfer function model are not required to be reserved, and only the model structure is reserved.
The principle of the preferred selection may be selected according to a predetermined criterion, for example, in terms of the characteristics of the amplitude-frequency characteristic curve corresponding to the transfer function model, and it is preferred to specify a characteristic selection value within a predetermined interval. The selection may also be based on the magnitude of the normalized root mean square error of the transfer function model, preferably within a predetermined range or closest to a predetermined value. Of course, the selection may also be based on the similarity between the normalized root mean square error of the transfer function model and the amplitude-frequency characteristic curve, and the set with the highest similarity is preferred. In a preferred embodiment, the selection of the optimization can be obtained according to the amplitude of the bode diagram of the transfer function model, specifically, the harmonic response analysis simulation of the mechanical model is realized through ANSYS simulation, and the intuitive proximity degree between the amplitude diagram obtained by the simulation analysis and the established amplitude diagram of the bode diagram of the transfer function model can be used for obtaining the intuitive proximity degree; another way is by how similar the magnitude of the bode plot of the transfer function model is to the calculated value of NRMSE of the transfer function model.
The method provided by the application is not limited to the optimization method, and the key point is that a plurality of optimal models are screened from the actual models obtained through modeling, the optimal models related to the actual models are obtained, and the optimal theoretical solution obtained by using priori knowledge is not used.
In step S4, an overall model structure is obtained by using the electrical part model structure and the derived model structure, the overall model structure is a transfer function model structure, a parameter estimation is performed by using the overall model structure to obtain an overall model, and a control parameter of the motor controller is obtained according to the overall model. In addition, because the transfer function model structure is selected in the process, the acquisition process of the whole model does not depend on the parameters obtained by the simulation part, the parameters can be set or obtained by parameter estimation before the control parameters of the motor controller are acquired, and compared with the parameters directly applied to the simulation process in the prior art, the method has no dependence on the parameters of the simulation.
According to the method, a complex electromechanical system is divided into a mechanical part and an electrical part, a model structure of the electrical part is obtained in a theoretical mode, a corresponding transfer function model is obtained through a rotary table actual model aiming at the mechanical part with a complex structure, a derived model structure of the mechanical part is further obtained, and finally the model structure of the electrical part is integrated to obtain an integral model structure.
The invention carries out modeling and analysis aiming at the complex mechanical structure, can effectively avoid the inaccuracy of pure theoretical analysis caused by less prior knowledge, and in the actual analysis process, the actual data is used as the input/output of system identification, so that the analysis result is more accurate and reliable.
Based on the above embodiments, obtaining a set of transfer function models including a plurality of transfer function models from a single set of frequency response data includes:
step S21, taking single group of frequency response data as input/output data, and using an auxiliary variable method to carry out system identification to obtain a transfer function model set G divided by orders and zero numbers;
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
wherein np represents the order, nz represents the number of zero points; np and nz are integers, and it is necessary to make nz < np.
It should be noted that the system identification is a system identification for a time domain or a frequency domain. np is the order and the number of pole points, np and nz are integers, and the specific upper limit value can be adjusted within a reasonable range.
In this embodiment, the system identification is performed by using an auxiliary variable method, and an input/output transfer function model, specifically, g(s), may be obtained by setting input/output data to the single group of frequency response data.
Meanwhile, different transfer function models G can be obtained due to different orders and zero numbers, and the orders and the zero numbers are divided, so that a set of the transfer function models G is obtained:
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
the transfer function models G can be used to obtain corresponding amplitude-frequency characteristic curves, normalized root mean square errors, and the like, and each transfer function model G(s) can be used to be combined with the transfer function model of the electrical part to obtain an overall transfer function model.
In a preferred embodiment, the selection of the order and the number of zero points is specified in step S21, where 1 ≦ np ≦ 20, np ∈ Z, 0 ≦ nz ≦ 19, nz ∈ Z, and nz < np; z is an integer. The reason why the np upper limit is 20 and the nz upper limit is 19 is that in common application cases, the order value and the zero value can meet the analysis requirement, and of course, the above values can be adjusted according to actual situations.
On the basis of the foregoing embodiment, the step of obtaining a preferred transfer function model from the transfer function model set in step S3 specifically includes the following steps:
and step S31, obtaining the amplitude-frequency characteristic curve and the normalized root mean square error of each transfer function model, and selecting an optimal transfer function model according to the similarity of the amplitude-frequency characteristic curve and the normalized root mean square error, wherein the transfer function model with high similarity is the optimal transfer function model.
According to the scheme, the obtained transfer function models GMachine for working(s) may be used to obtain corresponding amplitude-frequency characteristic curve, normalized root mean square error, etc., and the calculated value of normalized root mean square error may be similar or approximate to the image of amplitude-frequency characteristic curve, each transfer function model GMachine for working(s) all correspond to a similarity, all similarities can be compared, the similarity approaches 100%, and the transfer function model G is representedMachine for working(s) are more optimized, so that the optimized transfer function model G is selectedMachine for workingAnd(s) obtaining an optimal solution according to the similarity. Due to the transfer function model GMachine for workingThe total number of(s) is limited, so that the optimal solution of the similarity can be selected from the transfer function set formed by different orders and zero numbers.
Specifically, obtaining the normalized root mean square error of each transfer function model includes:
s301, acquiring two given matrixes x [ m, n ] and y [ m, n ] to obtain a normalized root mean square error NRMSE; the formula for NRMSE is:
wherein M and N are positive integers respectively representing rows and columns of the matrix x and y, M is the maximum value of M, and N is the maximum value of N.
Specifically, a uniform form of each transfer function model is obtained, as follows:
determination of the preferred transfer function model is chosen, where a in the general formula1、a2…anpm、b0、b1、b2…bnzmAre coefficients, npm is the order, nzm is the zero number, where Gnpm,nzm(s) meansThe obtained transfer function and the optimal solution thereof both have the form of the formula, and the value of each coefficient is determined according to the actual value by each transfer function.
In addition to the above embodiments, the method for obtaining an overall model structure by deriving a model structure and an electrical part model structure and obtaining an overall model by performing parameter estimation on the overall model structure in step S4 includes the following steps:
step S41, obtaining the optimized transfer function model structure Gm(s) obtaining an electrical part model structure Ge(s)；
Step S42, obtaining an integral model G (S);
wherein G(s) ═ Gm(s)·Ge(s)，Gm(s) is the derivation of the model structure, Ge(s) is an electrical part model structure;
performing parameter estimation on the integral model G(s) to obtain an integral model G'(s) containing parameters; a controller for controlling the motor by the integral model G'(s);
in addition, G iseAnd(s) is a common transfer function selected according to the type of the model of the electrical part, wherein the form and the value are empirical data obtained through a priori knowledge. Optionally, the above Ge(s) experimental data relating to reality, obtained by means of a precise analysis of the mechanical part, can also be selected.
And determining the model structure of the electrical part, wherein the model structure can be a transfer function, and specifically the model structure of the electrical part is determined by combining the priori knowledge and the analysis of the electrical link of the voice coil motor.
And then, determining the overall model structure of the complex electromechanical system together according to the model structures of the mechanical part and the electrical part of the system. Namely:
G(s)＝Gm(s)·Ge(s)
aiming at the selected types, the number of the pole points in the formula is npm +2, and the number of the zero points is nzm.
On the basis of the above embodiment, designing the control parameters of the motor controller according to the overall model, and controlling the motor controller to control the operation of the turntable according to the control parameters includes:
and setting the output current of the motor controller according to the integral model, and controlling the motor controller to output the output current to the voice coil motor so as to control the work of the rotary table.
It should be noted that, through the determination of the overall model structure, the motor controller of the voice coil motor may be designed according to the transfer function of the structure, so as to control the voice coil motor according to the motor controller.
Specifically, the transfer function of the overall structure may be analyzed to obtain characteristics of the transfer function, and the motor controller may be a PID controller with feedback control, and the motor controller may control the operation of the six-degree-of-freedom adjustment turntable.
The design of prior art motor controllers is typically based on a priori data, or a general design analysis. The motor controller designed by considering the characteristics in the transfer function has the characteristics of conforming to a six-degree-of-freedom adjusting turntable, so that the purpose of accurately controlling the voice coil motor can be achieved by setting design parameters, and for the motor controller with feedback control, the accuracy of the feedback control parameters is improved, and the control accuracy of the motor controller can be better improved.
Optionally, the motor controller is designed and adjusted, and the current output by the voice coil motor of the motor controller is mainly changed, that is, the current transmitted to the voice coil motor by the motor controller can be changed, so that the working state of the voice coil motor can be adjusted, and the working state of the voice coil motor can better meet the characteristics and the actual state of the current six-degree-of-freedom adjusting turntable.
The above method is mainly explained by motor control with feedback control, but of course, other types of motor controllers or control forms for the voice coil motor may be used.
The identification control method of the complex electromechanical system can effectively analyze and determine the model structure of the mechanical part of the system with complex system, high analysis difficulty and less prior knowledge, obtains the transfer function model of the complex electromechanical system based on identification, is applied to the synthesis of the whole model, and is suitable for the design of the system controller with higher precision requirement. The determination process of the model structure is that data obtained by the actual model is used as system identification input/output data, so that the method is more real and reliable compared with a method of directly identifying a system only by relying on finite element analysis data.
In addition to the identification control method for the complex electromechanical system provided by each of the above embodiments, the present application also provides an identification control system for a complex electromechanical system, which is applied to a six-degree-of-freedom adjustment turntable, where the six-degree-of-freedom adjustment turntable includes a turntable, six voice coil motors and a motor controller, the six voice coil motors being arranged at the lower part of the turntable; the identification control system comprises:
the classification acquisition module is used for dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part, acquiring a mechanical model of the mechanical part and acquiring the type of the electrical part to determine the model structure of the electrical part;
the mechanical transfer function model acquisition module is used for carrying out harmonic response analysis simulation on the mechanical model to obtain single group of frequency response data; obtaining a transfer function model set comprising a plurality of transfer function models according to the single group of frequency response data; the mechanical transfer function model acquisition module is connected with the classification acquisition module;
the optimization module is used for acquiring an optimal transfer function model from the transfer function model set and establishing a derived model structure of the mechanical part by using the optimal transfer function model; the optimization module is connected with the mechanical transfer function model acquisition module;
the fitting control module is used for obtaining an integral model structure by using the electric part model structure and the derived model structure, performing parameter estimation by using the integral model structure to obtain an integral model, designing control parameters of the motor controller according to the integral model, and controlling the motor controller to control the work of the rotary table according to the control parameters; the fitting control module is connected with the optimization module, the classification acquisition module and the motor controller.
It should be noted that the classification acquisition module is connected with the mechanical transfer function model, the mechanical transfer function model is connected with the optimization module, the optimization module is connected with the fitting control module, and the fitting control module is further connected with the classification acquisition module. The connection relations are all electric connection or signal connection and are used for data transmission.
And the classification acquisition module is used for performing the step S1 of the method, realizing the division of the functional form of the six-degree-of-freedom adjustment turntable, and obtaining model structures corresponding to the mechanical part and the electrical part respectively.
And the mechanical transfer function model acquisition module is used for performing step S2 of the method to realize harmonic response analysis simulation on the model corresponding to the mechanical part, and the module may be an ANSYS analysis module.
A preferred module for performing step S3 of the above method;
the fitting control module is configured to perform step S4 of the method, and specific functions and steps are not described herein again, please refer to the embodiment of the method.
On the basis of the above embodiment, the mechanical transfer function model acquisition module includes:
the first acquisition unit is used for acquiring single group of frequency response data and is connected with the classification acquisition module;
the system identification unit is connected with the acquisition unit and used for carrying out system identification by using a single group of frequency response data as input/output data and utilizing an auxiliary variable method to acquire a transfer function model set G divided by orders and zero numbers;
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
wherein np represents the order, nz represents the number of zero points; nz < np; np and nz are both integers.
It should be noted that the system identification is a system identification for a time domain or a frequency domain. np is the order and the number of pole points, np and nz are integers, and the specific upper limit value can be adjusted within a reasonable range.
Meanwhile, different transfer function models G(s) can be obtained due to different orders and zero numbers, and the orders and the zero numbers are divided to obtain a set of the transfer function models G(s):
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
the transfer function models G can be used for obtaining corresponding amplitude-frequency characteristic curves, normalized root mean square errors and the like, and meanwhile, each transfer function model G can be used for being combined with the transfer function of the electric part to obtain an integral transfer function model.
In a preferred embodiment, 1. ltoreq. np. ltoreq.20, np. ltoreq. Z, 0. ltoreq. nz. ltoreq.19, nz. ltoreq.Z, nz < np; z is an integer.
On the basis of the above embodiment, the preferred module includes:
the amplitude-frequency characteristic curve acquisition unit is used for acquiring the amplitude-frequency characteristic curve of each transfer function model;
a normalized root mean square error acquisition unit for normalized root mean square errors of the respective transfer function models;
the comparison unit is used for determining the similarity between the amplitude-frequency characteristic curve of the transfer function model and the normalized root mean square error, and taking the transfer function model with the maximum similarity as the preferred transfer function model; the comparison unit is connected with the amplitude-frequency characteristic curve acquisition unit and the standardized root-mean-square error acquisition unit.
Specifically, the amplitude-frequency characteristic curve acquisition unit and the normalized root mean square error acquisition unit are both connected with the comparison unit, and are used for sending the amplitude-frequency characteristic curve and the normalized root mean square error to the comparison unit, comparing the similarity, and obtaining a quantization value of the similarity. Each transfer function corresponds to a similarity quantization value, and the comparison unit selects the model with the highest similarity quantization value as the optimal solution.
In selecting optimized transfer function model GMachine for workingAnd(s) obtaining an optimal solution according to the similarity. Due to the transfer function model GMachine for workingThe total number of(s) is limited, so that the optimal solution of the similarity can be selected from the transfer function set formed by different orders and zero numbers.
Specifically, the normalized root mean square error obtaining unit is used for obtaining two given matrixes x [ m, n ] and y [ m, n ] to obtain normalized root mean square error NRMSE;
the formula for NRMSE is:
wherein M and N are positive integers respectively representing rows and columns of the matrix x and y, M is the maximum value of M, and N is the maximum value of N. The operation of obtaining the normalized root mean square error is prior art, i.e. obtained by a transfer function model.
The comparison unit obtains a uniform form of all the transfer function models before obtaining the optimal solution of each transfer function model, and the formula is as follows:
determination of the preferred transfer function model is chosen, where a in the general formula1、a2…anpm、b0、b1、b2…bnzmAre coefficients, npm is the order, nzm is the zero number, where Gnpm,nzmAnd(s) the obtained transfer function model and the optimal solution thereof are both in the form of the formula, and the value of each coefficient is determined according to the actual value by each transfer function model.
On the basis of the above embodiment, the fitting control module includes:
a fitting unit for obtaining a derived model structure Gm(s) obtaining an electrical part model structure Ge(s) and obtaining a global model g(s); wherein G(s) ═ Gm(s)·Ge(s)，Gm(s) is the derivation of the model structure, Ge(s) is an electrical part model; and performing parameter estimation by using the whole model structure G(s) to obtain a whole model G'(s) containing parameters.
And the control unit is connected with the fitting unit and used for designing the control parameters of the motor controller according to the integral model G'(s) containing the parameters and controlling the motor controller to control the work of the rotary table according to the control parameters.
And determining the model structure of the electrical part, wherein the model structure can be a transfer function, and specifically the model structure of the electrical part is determined by combining the priori knowledge and the analysis of the electrical link of the voice coil motor.
And then, determining the overall model structure of the complex electromechanical system together according to the model structures of the mechanical part and the electrical part of the system. Namely:
G(s)＝Gm(s)·Ge(s)
aiming at the selected types, the number of the pole points in the formula is npm +2, and the number of the zero points is nzm.
On the basis of the above embodiment, the control unit includes a current control unit, the current control unit is connected to the fitting unit, and controls the output current of the motor controller according to the global model G'(s), and controls the motor controller to output the output current to the voice coil motor so as to control the operation of the turntable
In this embodiment, the adjustment of the voice coil motor is specifically realized by controlling the change of the output current to the voice coil motor, so that the control mode of the voice coil motor more conforms to the characteristic of the current six-degree-of-freedom adjustment turntable.
In addition to the main steps of the method and the main structure of the system disclosed in the above embodiments, please refer to the prior art for common control methods and common structures of other parts, which are not described herein again.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The identification control method and system for the complex electromechanical system provided by the invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (6)
1. The identification control method of the complex electromechanical system is applied to a six-degree-of-freedom adjusting rotary table, wherein the six-degree-of-freedom adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, wherein the voice coil motor and the motor controller are arranged at the lower part of the rotary table; characterized in that the method comprises:
dividing a six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part, acquiring a mechanical model of the mechanical part, and acquiring the type of the electrical part to determine the model structure of the electrical part;
carrying out harmonic response analysis simulation on the mechanical model to obtain single group of frequency response data; obtaining a transfer function model set comprising a plurality of transfer function models according to the single group of frequency response data;
acquiring a preferred transfer function model from the transfer function model set, and establishing a derived model structure of the mechanical part by using the preferred transfer function model;
obtaining an integral model structure by using the electric part model structure and the derived model structure, performing parameter estimation by using the integral model structure to obtain an integral model, designing control parameters of the motor controller according to the integral model, and controlling the motor controller to control the work of the rotary table according to the control parameters;
obtaining a set of transfer function models including a number of the transfer function models from the single set of frequency response data comprises:
taking the single group of frequency response data as input/output data, and performing system identification by using an auxiliary variable method to obtain the transfer function model set G divided by orders and zero numbers;
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
wherein np represents the order, nz represents the number of zero points; nz < np; np and nz are integers;
obtaining a preferred transfer function model in the set of transfer function models, comprising:
and acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model, and selecting an optimal transfer function model according to the similarity of the amplitude-frequency characteristic curve and the normalized root mean square error, wherein the transfer function model with high similarity is the optimal transfer function model.
2. The identification control method of the complex electromechanical system according to claim 1, wherein obtaining an overall model structure from the derived model structure and the electrical part model structure, and obtaining an overall model from the overall model structure by performing parameter estimation comprises:
obtaining the preferred transfer function model structure Gm(s) obtaining the electrical part model structure Ge(s)；
Obtaining an overall model structure G(s);
wherein G(s) ═ Gm(s)·Ge(s)，Gm(s) is the structure of the derived model, Ge(s) is the electrical part model structure;
carrying out parameter estimation on the whole model structure G(s) to obtain a parameter-containing whole model G'(s); for controlling said motor controller by said integral model G'(s) containing parameters.
3. The identification control method of the complex electromechanical system according to claim 2, wherein designing control parameters of the motor controller according to the integral model, and controlling the motor controller to control the operation of the turntable according to the control parameters comprises:
and setting the output current of the motor controller according to the integral model, and controlling the motor controller to output the output current to the voice coil motor so as to control the work of the rotary table.
4. The identification control system of the complex electromechanical system is applied to a six-degree-of-freedom adjusting rotary table, and the six-degree-of-freedom adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, wherein the voice coil motor and the motor controller are arranged at the lower part of the rotary table; characterized in that, the control system of discerning includes:
the classification acquisition module is used for dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part, acquiring a mechanical model of the mechanical part and acquiring the type of the electrical part to determine the model structure of the electrical part;
the mechanical transfer function model acquisition module is used for carrying out harmonic response analysis simulation on the mechanical model to obtain single group of frequency response data; obtaining a transfer function model set comprising a plurality of transfer function models according to the single group of frequency response data; the mechanical transfer function model acquisition module is connected with the classification acquisition module;
the optimization module is used for acquiring an optimized transfer function model from the transfer function model set and establishing a derived model structure of the mechanical part by using the optimized transfer function model; the optimal module is connected with the mechanical transfer function model acquisition module;
the fitting control module is used for obtaining an integral model structure by using the electric part model structure and the derived model structure, performing parameter estimation by using the integral model structure to obtain an integral model, obtaining control parameters of the motor controller according to the integral model, and controlling the motor controller to control the work of the rotary table according to the control parameters; the fitting control module is connected with the optimal selection module, the classification acquisition module and the motor controller;
the mechanical transfer function model acquisition module includes:
the first acquisition unit is used for acquiring the single group of frequency response data and is connected with the classification acquisition module;
the system identification unit is connected with the acquisition unit and used for carrying out system identification by using the single group of frequency response data as input/output data and utilizing an auxiliary variable method to acquire the transfer function model set G divided by orders and zero numbers;
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
wherein np represents the order, nz represents the number of zero points; nz < np; np and nz are integers;
the preferred module comprises:
an amplitude-frequency characteristic curve obtaining unit, configured to obtain an amplitude-frequency characteristic curve of each transfer function model;
a normalized root mean square error acquisition unit for obtaining normalized root mean square errors of the transfer function models;
the comparison unit is used for determining the similarity between the amplitude-frequency characteristic curve of the transfer function model and the normalized root mean square error, and taking the transfer function model with the maximum similarity as the preferred transfer function model; and the comparison unit is connected with the amplitude-frequency characteristic curve acquisition unit and the standardized root-mean-square error acquisition unit.
5. The recognition control system of the complex electromechanical system according to claim 4, wherein the fitting control module comprises:
a fitting unit for obtaining the derived model structure Gm(s) obtaining the electrical part model structure Ge(s) and obtaining the overall model structure g(s); wherein G(s) ═ Gm(s)·Ge(s)，Gm(s) is the structure of the derived model, Ge(s) is the electrical part model structure; carrying out parameter estimation on the whole model structure G(s) to obtain a whole model G'(s) containing parameters;
and the control unit is connected with the fitting unit and used for acquiring the control parameters of the motor controller according to the integral model G'(s) and controlling the motor controller to control the work of the rotary table according to the control parameters.
6. An identification control system of a complex electromechanical system according to claim 5, characterised in that said control unit comprises:
and the current control unit is connected with the fitting unit, controls the output current of the motor controller according to the integral model G'(s), and controls the motor controller to output the output current to the voice coil motor so as to control the work of the rotary table.
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