CN110968118A - Control method for six-degree-of-freedom adjusting rotary table - Google Patents
Control method for six-degree-of-freedom adjusting rotary table Download PDFInfo
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Abstract
The invention discloses a control method of a six-degree-of-freedom adjusting turntable, which comprises the following steps: dividing the six-degree-of-freedom adjusting rotary table into mechanical parts to perform harmonic response analysis so as to obtain N groups of frequency response data under different inputs, performing system identification on one group of frequency response data, and establishing a set of first transfer function models with different order-zero point number combinations; performing system identification on the N groups of frequency response data, and selecting an optimal transfer function model from the set of second transfer function models as a mechanical transfer function model; setting a preset order and a preset zero point number of an electrical part, carrying out system identification on a plurality of groups of preset signals, and establishing a set of third transfer function models of the preset order and the preset zero point number and an electrical transfer function model; and acquiring and controlling the control parameters of the motor controller according to the integral transfer function, and controlling the motor controller to control the work of the rotary table according to the control parameters. The invention can realize the accurate control of the complex system.
Description
Technical Field
The invention relates to the technical field of system identification, in particular to a control method of a six-degree-of-freedom adjusting turntable.
Background
The existing traditional system identification technology mainly comprises the following processes: designing an identification experiment according to an identification purpose and system prior knowledge, preprocessing input/output data after the input/output data are acquired, determining a model structure according to the prior knowledge and the identification purpose, performing parameter estimation by using a mathematical method, and establishing a transfer function model of the system.
For complex systems, especially complex electromechanical systems, the difficulty of the conventional identification method is: due to the complexity of the system itself and the high requirement for control accuracy, one of the core steps in the identification process is: the conventional method for determining the model structure has an unsatisfactory effect, and is difficult to accurately give the model structure of the system, so that the subsequent system identification and the work of designing the controller based on the transfer function model are influenced.
In the prior art, for some complex pure mechanical structures, in analysis, a system entity can be disassembled into a plurality of simple structures, an experiment is respectively designed for each part, and after input/output data is obtained, a transfer function is established by using system identification. However, for some high-precision mechanical structures, the test is not convenient to disassemble, so that even from a theoretical perspective, the system can be decomposed into a plurality of links, but the integral mechanical structure which cannot be disassembled causes difficulty in accurate measurement of actual data of each link.
In summary, how to provide a control method for a six-degree-of-freedom adjustment turntable, which can achieve accurate control, is a problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a method for controlling a six-degree-of-freedom adjustment turntable, which can achieve precise control of the six-degree-of-freedom adjustment turntable.
In order to achieve the above purpose, the invention provides the following technical scheme:
a control method of a six-degree-of-freedom adjusting rotary table is applied to the six-degree-of-freedom adjusting rotary table, the six-degree-of-freedom adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, and the control method comprises the following steps:
dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part;
performing harmonic response analysis on the mechanical part to obtain N groups of frequency response data under different inputs, performing system identification on one group of frequency response data, establishing a set of first transfer function models with different order-zero point combinations, selecting an optimal transfer function model from the set of first transfer function models, and taking the order as a target order and the zero point as a target zero point;
performing system identification on the N groups of frequency response data, establishing a set of second transfer function models of the target order and the target zero point number, and selecting an optimal transfer function model from the set of second transfer function models as a mechanical transfer function model; the mechanical transfer function model is a transfer function model containing parameters;
setting a preset order and a preset zero point number of the electrical part, carrying out system identification on a plurality of groups of preset signals, establishing a set of third transfer function models of the preset order and the preset zero point number, and selecting an optimal transfer function model from the set of the third transfer function models to serve as an electrical transfer function model; the electric transfer function model is a transfer function model containing parameters;
and obtaining an integral transfer function model according to the mechanical transfer function model and the electrical transfer function model, controlling according to the integral transfer function model to obtain control parameters of the motor controller, and controlling the motor controller to control the work of the rotary table according to the control parameters.
Preferably, acquiring different inputs of the N sets of frequency response data at different inputs comprises: different frequencies or different magnitudes of force.
Preferably, the system identification is performed on a set of the frequency response data, and a set of first transfer function models with different order-zero point number combinations is established, including:
selecting one set of the frequency response data among the N sets of the frequency response data;
and carrying out system identification on the selected frequency response data by using an auxiliary variable method, and establishing a plurality of transfer function models, wherein the combination of the order-zero point number of the transfer function models is different so as to form a set of the first transfer function models.
Preferably, selecting a preferred transfer function model from the set of first transfer function models and determining a target order and a target number of zero points includes:
acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the first set of transfer function models, 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 the large similarity is the optimal transfer function model;
and acquiring the order of the preferred transfer function model as the target order, and taking the number of zero points of the preferred transfer function model as the target number of zero points.
Preferably, the system identification is performed on N sets of the frequency response data, and a set of a second transfer function model of the target order and the target zero point number is established, including:
carrying out system identification on each group of frequency response data by using an auxiliary variable method;
and establishing a plurality of sets of second transfer function models of the transfer function models with the target order as the order and the target zero point number as the zero point number, wherein the order of each transfer function model is the target order and the zero point number is the target zero point number.
Preferably, selecting a preferred transfer function model from the set of second transfer function models as a mechanical transfer function model comprises:
and acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the set of the second transfer function models, 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 mechanical transfer function model.
Preferably, the setting of the preset order and the preset zero point number of the electrical part includes:
determining a transfer function model of the electrical part according to the type of the electrical part, and acquiring an order corresponding to the transfer function model of the electrical part as the preset order and a corresponding zero point number as the preset zero point number.
Preferably, system identification is carried out on a plurality of groups of preset signals, a set of third transfer function models of the preset orders and the preset zero points is established, and an optimal transfer function model is selected from the set of the third transfer function models and is used as an electric transfer function model; the method comprises the following steps:
respectively taking the voltage signal and the output displacement as the input/output quantity of a preset signal, taking the preset signal as input/output data, and establishing a set of a third transfer function model of the preset order and the preset zero point number by using an auxiliary variable method;
and acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the set of the third transfer function models, 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 the large similarity is the electric transfer function model.
Obtaining control parameters of the motor controller according to the control of the integral transfer function model, and controlling the motor controller to control the work of the rotary table according to the control parameters, wherein the control parameters comprise:
and controlling the output current of the motor controller according to the integral transfer function 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 invention reasonably splits the complex mechanical-electrical system into a mechanical part and an electrical part, and establishes a transfer function model for the mechanical part by means of frequency response analysis, thereby reducing the difficulty of determining the model structure of the complex system in the system identification method; provides an effective solution for analyzing a system with a complex structure and difficult input/output data measurement.
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 control method for a six-degree-of-freedom adjustment turntable according to the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention;
FIG. 3 is a schematic diagram of a partitioned six-degree-of-freedom adjustment turret provided by the present invention;
FIG. 4 is a front view of a motor control system for a six degree-of-freedom turret;
FIG. 5 is a top view of a motor control system for a six degree-of-freedom turret;
fig. 6 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 a control method of the six-degree-of-freedom adjusting rotary table, which can realize accurate control of the six-degree-of-freedom adjusting rotary table and has good control effect.
Referring to fig. 1 to 6, fig. 1 is a flowchart illustrating a control method for a six-degree-of-freedom adjustment turret according to the present invention; FIG. 2 is a flow chart of an embodiment of the present invention; FIG. 3 is a schematic diagram of a partitioned six-degree-of-freedom adjustment turret provided by the present invention; FIG. 4 is a front view of a motor control system for a six degree-of-freedom turret; FIG. 5 is a top view of a motor control system for a six degree-of-freedom turret; fig. 6 is a left side view of a motor control system of a six degree-of-freedom turret.
The application provides a control method of a six-degree-of-freedom adjusting rotary table, which is applied to the 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, and the control method comprises the following steps:
step S1, dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part;
step S2, carrying out harmonic response analysis on the mechanical part to obtain N sets of frequency response data under different inputs, carrying out system identification on a set of frequency response data, establishing a set of first transfer function models with different order-zero point combinations, selecting an optimal transfer function model from the set of first transfer function models, and taking the order as a target order and the zero point as a target zero point;
s3, performing system identification on the N groups of frequency response data, establishing a set of second transfer function models with target orders and target zero points, and selecting an optimal transfer function model from the set of second transfer function models as a mechanical transfer function model;
step S4, setting a preset order and a preset zero point number of the electric part, carrying out system identification on a plurality of groups of preset signals, establishing a set of third transfer function models with the preset order and the preset zero point number, and selecting an optimal transfer function model from the set of the third transfer function models as an electric transfer function model;
and step S5, obtaining an integral transfer function model according to the mechanical transfer function model and the electrical transfer function model, controlling control parameters of the motor controller according to the integral transfer function model, and controlling the motor controller to control the work of the rotary table according to the control parameters.
Referring to fig. 4 to 6, a supporting and adjusting leg is disposed below the turntable 1, and the turntable 1 is supported and adjusted in 6 directions, with the supporting force shown in the figureIn each case are F1、F2、F3、F4、F5And F6。
The 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 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 analysis and the examination of the two parts.
In step S2, the mechanical part may be modeled to obtain a mechanical model, where the mechanical model refers to a three-dimensional structural model of the mechanical part, and may be modeled and analyzed by software. For the modeled mechanical model, harmonic response analysis simulations can be performed, which are used to determine the steady-state response of a linear structure when subjected to a load that varies sinusoidally with time, with the aim of calculating the response of the structure versus frequency at several frequencies, the data obtained and used in this application being bode plots, i.e. logarithmic frequency response curves, including amplitude and phase angle plots. Harmonic response analysis simulations are applicable to the analytical version of the turntable in this application.
The harmonic response analysis process can input different types or values of input values to obtain N groups of frequency response data, only one group of frequency response data is subjected to system identification, a target number of first transfer function models are obtained, and a set of the first transfer function models is formed.
The input values of different types or values comprise different frequencies and the like, so that a frequency response data set established by N groups of different input values is obtained.
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.
Optionally, the frequency setting may be 1-1000Hz in this application, so that N sets of frequency response data are obtained.
In the process of the system identification, transfer function models with different orders and zero point combinations need to be established to form a plurality of selectable transfer function models, wherein the transfer function models can include coefficients or can be transfer function model structures without coefficients, so that a set of transfer function models is formed, and the set is called as a set of first transfer function models. In the process, the system identification can be carried out by an auxiliary variable method in the prior art. Since the set of transfer function models and the set of transfer function model structures differ only in whether or not there are coefficients that are not required in the subsequent steps, the case of the set of transfer function models and the set of transfer function model structures is also included in this range, which is expressed herein as the set of transfer function models.
And obtaining a set of an optimal transfer function model through optimization selection, thereby determining a target order and a target zero point number.
In step S3, performing system identification on the N sets of frequency response data obtained in step 2, establishing a transfer function model with the target order as an order and the target zero point number as a zero point number, forming a set, performing optimization selection, and finally determining a mechanical transfer function model. The transfer function model obtained in step S3 is required to include parameters, and is not a transfer function model structure.
In step S4, a preset order and a preset number of zero points of the electrical part are set, and an electrical part model corresponding to the type of the electrical part, optionally a transfer function model of the electrical part mentioned later, may be determined according to the priori knowledge and theoretical analysis, because the mechanical part may also obtain a final result using the transfer function model, a final overall transfer function model may be obtained by operating the two transfer functions.
Carrying out system identification on a plurality of groups of preset signals, wherein the preset signals can comprise voltage signals and the like, establishing a set of third transfer function models taking the preset order and the preset zero point number as standards, selecting an optimal transfer function model from the set of the third transfer function models as an electric transfer function model, and the electric transfer function model is a transfer function model containing parameters; this process is similar to the above process and will not be described further herein.
The principle of the optimization 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 preferable to specify the 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 the application, because the mechanical part in the integral model is obtained according to the analysis of the actual model, the obtained control parameters can control the motor controller to work according to the control parameters aiming at the actual structure of the rotary table system, and the control of the voice coil motor on the rotary table can be more in line with the actual characteristics of the rotary table.
The invention reasonably splits the complex mechanical-electrical system into a mechanical part and an electrical part, and for the mechanical part which is inconvenient to further split, the invention reduces the difficulty of determining the model structure of the complex system in the system identification method by means of frequency response analysis and establishment of a transfer function model, and provides an effective solution for analyzing the system with a complex structure and difficult input/output data measurement.
In step S2, different inputs in the N sets of frequency response data obtained under different inputs include different frequencies or different magnitudes of applied force.
In step S2, the method for performing system identification on a group of frequency response data and establishing a set of first transfer function models with different order-zero point combinations specifically includes the following steps:
step S21, selecting one set of frequency response data from the N sets of frequency response data;
and step S22, performing system identification on the selected frequency response data by using an auxiliary variable method, and establishing a plurality of transfer function models, wherein the combination of the order-zero point number of the transfer function models is different, so as to form a first transfer function model set.
Specifically, a certain group of frequency response data is respectively used as input/output data, an auxiliary variable method (IV) is used for carrying out system identification on a mechanical part of the system, and a transfer function model set G with different orders and zero numbers is established.
G＝{G1,0(s),G2,0(s),G2,1(s)...Gnp,nz-1(s),Gnp,nz(s)}
Wherein np represents the order, or named as the number of pole points, nz represents the number of zero points, nz is less than np, and the specific upper limit value can be adjusted within a reasonable range. It should be noted that, the values of the above order and the zero point number may be: np is more than or equal to 1 and less than or equal to 20, np belongs to Z, nz is more than or equal to 0 and less than or equal to 19, nz belongs to Z, and 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.
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(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)}
specifically, the above harmonic response analysis of the mechanical part to obtain N sets of frequency response data under different inputs includes: and (3) carrying out harmonic response analysis simulation on the mechanical part model established in ANSYS finite element analysis software, setting a frequency range, and analyzing and acquiring N groups of frequency response data under different inputs.
The frequency range may be 1-1000 Hz.
In step S2, the method for selecting a preferred transfer function model from the set of first transfer function models and determining a target order and a target zero point number specifically includes the following steps:
step S23, obtaining an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the first set of transfer function models, 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;
and step S24, acquiring the order of the preferred transfer function model as a target order, and taking the zero point number of the preferred transfer function model as a target zero point number.
Each obtained transfer function model g(s) can be used to obtain a corresponding amplitude-frequency characteristic curve, normalized root mean square error, and the like, and a calculated value of the normalized root mean square error and an image of the amplitude-frequency characteristic curve can obtain similarity or approximation, each transfer function model g(s) corresponds to a similarity, all the similarities can be compared, the similarity approaches to 100%, and the model characteristics representing the transfer function models g(s) are more optimized, so that when the optimized transfer function model g(s) is selected, an optimal solution can be obtained according to the similarities. Since the total number of the transfer function models g(s) is limited, the optimal solution of the similarity can be selected from the transfer function model sets formed by different orders and zero numbers.
Specifically, obtaining the normalized root mean square error of each transfer function model comprises obtaining two given matrixes x [ m, n ] and y [ m, n ], and obtaining the normalized root mean square error NRMSE.
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,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.
And extracting the order and the zero point number of the obtained optimally selected transfer function model, and respectively using the order and the zero point number as a target order and a target zero point number.
In step S3, the step of performing system identification on the N sets of frequency response data and establishing a set of second transfer function models of a target order and a target zero point number specifically includes the following steps:
step S31, performing system identification on each group of frequency response data by using an auxiliary variable method;
step S32, a set of a plurality of second transfer function models of the transfer function models with the target order as the order and the target zero point as the zero point is established, where the order of each transfer function model is the target order and the zero point is the target zero point.
It should be noted that, in step S3, the system identification is performed for the second time, and the system identification is performed again for each set of frequency response data by the auxiliary variable method, and the order and the number of zero points for the system identification performed this time are not selected according to the permutation and combination of the given range in the above embodiment, but the target order and the target number of zero points are used as the transfer function model, so that the target order and the target number of zero points need to be obtained.
The optimal target order and the optimal number of zero points obtained by the optimization are the optimal order and the number of zero points, and the step S3 is a transfer function model for determining the order and the number of zero points obtained by performing system identification on all frequency response data through the optimal order and the optimal number of zero points.
Optionally, in step S3, selecting a preferred transfer function model from the set of second transfer function models as the mechanical transfer function model includes:
step S33, obtaining the amplitude-frequency characteristic curve and the normalized root mean square error of each transfer function model in the second transfer function model set, and selecting an optimal transfer function model according to the similarity degree of the amplitude-frequency characteristic curve and the normalized root mean square error, wherein the transfer function model with the large similarity degree is a mechanical transfer function model Gm(s)。
The preferred mode of selecting the transfer function model is the same as the optimization mode of the first set of transfer function models, and is not described herein again.
Optionally, in step S4, the setting of the preset order and the preset zero point number of the electrical part includes:
and step S41, determining the transfer function model of the electrical part according to the type of the electrical part, and acquiring the order corresponding to the transfer function model of the electrical part as a preset order and the corresponding zero point number as a preset zero point number.
And determining an electrical part model structure, wherein the model structure is a transfer function model, and specifically, the electrical part model structure is determined by combining priori knowledge and analysis of the electrical link of the voice coil motor.
And obtaining an order and a zero point number through the transfer function model of the electrical part, and respectively using the order and the zero point number as a preset order and a preset zero point number.
Optionally, in step S4, performing system identification on multiple groups of preset signals, establishing a set of third transfer function models with the preset order and the preset number of zero points, and selecting a preferred transfer function model from the set of third transfer function models as an electrical transfer function model; the method comprises the following steps:
step S42, respectively taking the voltage signal and the output displacement as the input/output quantity of a preset signal, taking the preset signal as input/output data, and establishing a set of a third transfer function model of the preset order and the preset zero point number by using an auxiliary variable method;
and step S43, obtaining the amplitude-frequency characteristic curve and the normalized root mean square error of each transfer function model in the third set of transfer function models, and selecting a preferred 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 the large similarity is the electric transfer function model.
Specifically, the third transfer function model is determined in the same manner as described above, using multiple sets of input values as the preset signal, wherein the voltage signal may be a sinusoidal signal, a random white gaussian noise signal, or the like.
Optionally, in step S5, obtaining an overall transfer function model according to the mechanical transfer function model and the electrical transfer function model includes:
and step S51, acquiring a mechanical transfer function model and an electrical transfer function model, and acquiring an integral transfer function model of the six-degree-of-freedom adjusting turntable according to the mechanical transfer function model and the electrical transfer function model.
The method for obtaining the integral model by the derived model and the electric part model specifically comprises the following steps:
obtaining a preferred transfer function model Gm(s) obtaining an electric part model Ge(s)；
Obtaining an overall model G(s); wherein:
G(s)＝Gm(s)·Ge(s)
Gm(s) is a preferred transfer function model, Ge(s) is an electrical part model.
Optionally, in step S5, obtaining a control parameter of the motor controller according to the overall transfer function model control, and controlling the motor controller to control the operation of the turntable according to the control parameter, including:
and step S53, controlling the output current of the motor controller according to the integral transfer function 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 controlled to operate 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 model 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 motor controllers of the prior art are typically set up on a priori basis, or are analyzed in general. The motor controller obtained by considering the characteristics in the transfer function model 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 parameters, and for the motor controller with feedback control, the accuracy of feedback control parameters is improved, and the control accuracy of the motor controller can be better improved.
Optionally, the motor controller is 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 is adjusted, and the working state of the voice coil motor is more consistent with 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 invention divides a complex electromechanical system into a mechanical part and an electrical part, models the complex mechanical part in the system by using ANSYS finite element analysis software, performs harmonic response analysis, and establishes a system transfer function model of the mechanical part by using an auxiliary variable method (IV) based on the obtained frequency response data. And determining a system model structure of a simple electrical part in the system based on prior knowledge, and establishing a transfer function model of the electrical part by using an auxiliary variable method (IV) directly according to experimental data. And finally, calculating the whole transfer function model of the system according to the transfer function models of the mechanical part and the electrical part of the system.
The method has the advantages that the transfer function model is established by means of finite element analysis, and the difficulty in determining the complex system model structure in the system identification method is reduced; provides an effective solution for analyzing a system with a complex structure and difficult input/output data measurement.
Except for the main steps and the internal relationship of the control method for a six-degree-of-freedom adjustment turntable provided in the above embodiments, the calculation and synthesis modes of the transfer function model included therein, and the contents of other parts are referred to the prior art, and 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 control method for the six-degree-of-freedom adjusting turntable provided by the invention is 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 (9)
1. A control method of a six-freedom-degree adjusting rotary table is applied to the six-freedom-degree adjusting rotary table, the six-freedom-degree adjusting rotary table comprises a rotary table, a voice coil motor and a motor controller, and the control method is characterized by comprising the following steps of:
dividing the six-degree-of-freedom adjusting turntable into a mechanical part and an electrical part;
performing harmonic response analysis on the mechanical part to obtain N groups of frequency response data under different inputs, performing system identification on one group of frequency response data, establishing a set of first transfer function models with different order-zero point combinations, selecting an optimal transfer function model from the set of first transfer function models, and taking the order as a target order and the zero point as a target zero point;
performing system identification on the N groups of frequency response data, establishing a set of second transfer function models of the target order and the target zero point number, and selecting an optimal transfer function model from the set of second transfer function models as a mechanical transfer function model; the mechanical transfer function model is a transfer function model containing parameters;
setting a preset order and a preset zero point number of the electrical part, carrying out system identification on a plurality of groups of preset signals, establishing a set of third transfer function models of the preset order and the preset zero point number, and selecting an optimal transfer function model from the set of the third transfer function models to serve as an electrical transfer function model; the electric transfer function model is a transfer function model containing parameters;
and obtaining an integral transfer function model according to the mechanical transfer function model and the electrical transfer function model, controlling according to the integral transfer function model to obtain control parameters of the motor controller, and controlling the motor controller to control the work of the rotary table according to the control parameters.
2. The method of claim 1, wherein obtaining different inputs of the N sets of frequency response data at different inputs comprises: different frequencies or different magnitudes of force.
3. The method as claimed in claim 2, wherein the step of performing systematic identification on a set of the frequency response data to establish a set of first transfer function models with different order-zero point combinations comprises:
selecting one set of the frequency response data among the N sets of the frequency response data;
and carrying out system identification on the selected frequency response data by using an auxiliary variable method, and establishing a plurality of transfer function models, wherein the combination of the order-zero point number of the transfer function models is different so as to form a set of the first transfer function models.
4. The method as claimed in claim 3, wherein selecting the preferred transfer function model from the set of first transfer function models and determining the target order and the number of target zeros comprises:
acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the first set of transfer function models, 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 the large similarity is the optimal transfer function model;
and acquiring the order of the preferred transfer function model as the target order, and taking the number of zero points of the preferred transfer function model as the target number of zero points.
5. The method as claimed in claim 4, wherein the step of performing systematic identification on the N sets of frequency response data to establish a set of second transfer function models of the target order and the target zero point number comprises:
carrying out system identification on each group of frequency response data by using an auxiliary variable method;
and establishing a plurality of sets of second transfer function models of the transfer function models with the target order as the order and the target zero point number as the zero point number, wherein the order of each transfer function model is the target order and the zero point number is the target zero point number.
6. The method of claim 1, wherein selecting a preferred transfer function model from the set of second transfer function models as the mechanical transfer function model comprises:
and acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the set of the second transfer function models, 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 mechanical transfer function model.
7. The method as claimed in any one of claims 1 to 6, wherein setting the number of the predetermined orders and the predetermined zero points of the electrical part comprises:
determining a transfer function model of the electrical part according to the type of the electrical part, and acquiring an order corresponding to the transfer function model of the electrical part as the preset order and a corresponding zero point number as the preset zero point number.
8. The method as claimed in claim 7, wherein the method comprises performing system identification on a plurality of groups of preset signals, establishing a set of third transfer function models of the preset order and the preset number of zero points, and selecting a preferred transfer function model from the set of third transfer function models as an electrical transfer function model; the method comprises the following steps:
respectively taking the voltage signal and the output displacement as the input/output quantity of a preset signal, taking the preset signal as input/output data, and establishing a set of a third transfer function model of the preset order and the preset zero point number by using an auxiliary variable method;
and acquiring an amplitude-frequency characteristic curve and a normalized root mean square error of each transfer function model in the set of the third transfer function models, 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 the large similarity is the electric transfer function model.
9. The method as claimed in claim 8, wherein the controlling the turntable with six degrees of freedom according to the overall transfer function model comprises:
and controlling the output current of the motor controller according to the integral transfer function 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.
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