CN116991068A - Motor control method and system based on distributed preset time gradient descent method - Google Patents
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Abstract
The invention relates to the technical field of permanent magnet synchronous motor control, and provides a motor control method and system based on a distributed preset time gradient descent method, wherein the method comprises the steps of establishing a state space model with unknown nonlinearity; establishing a fuzzy logic approximation model, and determining the self-adaptive law of a fuzzy logic system based on a gradient descent optimization algorithm; constructing a preset time fuzzy controller; the unknown nonlinear characteristics of the motor servo system can be approximated by the fuzzy logic approximation model, learning factors in the self-adaptive law are adjusted, and the fuzzy basis function of the fuzzy logic approximation model is adaptively adjusted based on the change of the acquired operation data of the motor servo system, so that the design parameters of the time fuzzy controller are adjusted, the convergence time of the fuzzy control is obtained, and the state space model is solved to obtain specific motor control actions. By introducing a preset time concept and a fuzzy logic control strategy based on a gradient descent method, accurate control and rapid convergence of a motor servo system can be realized.
Description
Technical Field
The disclosure relates to the technical field of permanent magnet synchronous motor control, in particular to a motor control method and system based on a distributed preset time gradient descent method.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of modern industry, motor servo systems play an important role in the field of automation control. The motor servo system is widely applied to the fields of robots, industrial production lines, medical equipment and the like and is used for realizing accurate position control and motion control. The permanent magnet synchronous motor is widely applied as a high-performance motor because of the advantages of quick response, high efficiency, high torque density and the like.
The inventors found in the study that the conventional motor servo control method often has some problems. First, motor servo systems have complex nonlinear characteristics, including motor nonlinearity, load nonlinearity, sensor nonlinearity, etc., which adversely affect the control performance of the system, resulting in longer convergence times and larger tracking errors. To overcome these challenges, a preset time control method is introduced into the motor servo system. The core idea of the preset time control is to match the design and analysis of the control system with the required response speed and performance requirements by predefining the convergence time of the system, so that the application range is wide. This approach allows the system to achieve good performance without requiring an accurate model. However, the existing finite/fixed time control method is often limited in the stability time by initial conditions and adjustment parameters, and is often difficult to obtain when the system is complex, so that the method is not beneficial to practical application.
Second, there is often uncertainty in the parameters of the motor servo system, such as parameter drift due to temperature variations, wear and aging, and the like. These uncertainty factors can cause stability and robustness challenges to the control system, affecting the reliability and performance of the system. In the control of a motor servo system, fuzzy control is a commonly used method of nonlinearity. Fuzzy control maps fuzzy input and output to specific control actions by establishing a fuzzy rule and a fuzzy logic-based reasoning mechanism, so as to realize control of the system. However, the conventional fuzzy control algorithm has a plurality of defects when processing unknown nonlinear functions, cannot accurately process the influence of nonlinearity, and may cause unstable system and influence the working efficiency.
Disclosure of Invention
In order to solve the problems, the present disclosure provides a motor control method and system based on a distributed preset time gradient descent method, which overcomes the nonlinearity and uncertainty factors in a motor servo system, and achieves faster convergence speed and higher control accuracy.
In order to achieve the above purpose, the present disclosure adopts the following technical scheme:
one or more embodiments provide a motor control method based on a distributed preset time gradient descent method, including the steps of:
establishing a permanent magnet synchronous motor kinematic equation, and converting the equation into a corresponding state space model with unknown nonlinearity;
establishing a fuzzy logic approximation model, and determining the self-adaptive law of the fuzzy logic approximation model based on a gradient descent optimization algorithm;
constructing a preset time fuzzy controller based on the constructed state space model;
the unknown nonlinear characteristics of the motor servo system can be approximated by the fuzzy logic approximation model, learning factors in the self-adaptive law are adjusted, and the fuzzy basis function of the fuzzy logic approximation model is self-adaptively adjusted based on the acquired change of the operation data of the motor servo system;
and according to the obtained fuzzy base function and the learning factor, adjusting design parameters of the time fuzzy controller to obtain convergence time of fuzzy control, and solving a state space model to obtain motor control actions.
One or more embodiments provide a motor control system based on a distributed preset time gradient descent method, including:
a first construction module: the system is configured to establish a permanent magnet synchronous motor kinematic equation and convert the equation into a corresponding state space model with unknown nonlinearity;
and a second construction module: the method comprises the steps of being configured to establish a fuzzy logic approximation model and determining an adaptive law of the fuzzy logic approximation model based on a gradient descent optimization algorithm;
and a third construction module: the system comprises a state space model, a preset time fuzzy controller and a control unit, wherein the state space model is used for building the state space model;
and the self-adaptive adjustment module is used for: the fuzzy logic approximation model is configured to be capable of approximating unknown nonlinear characteristics of the motor servo system with the fuzzy logic approximation model, adjust learning factors in the adaptive law, and adaptively adjust a fuzzy basis function of the fuzzy logic approximation model based on changes of the acquired operation data of the motor servo system;
the control action output module: the method is configured to be used for adjusting design parameters of the time fuzzy controller according to the obtained fuzzy base function and the learning factor, obtaining convergence time of fuzzy control, and solving the state space model to obtain motor control actions.
An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of a motor control method based on a distributed pre-set time gradient descent method as described above.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of a motor control method based on a distributed pre-set time gradient descent method as described above.
Compared with the prior art, the beneficial effects of the present disclosure are:
the method and the device can realize that the convergence time is irrelevant to initial conditions, and allow independent parameters to freely pre-specify the convergence time, so that the accurate control of the motor servo system and the rapid convergence of preset time are realized. The innovative method not only improves the dynamic performance and tracking precision of the system, but also enhances the adaptability of the system to uncertainty and nonlinear characteristics, and brings remarkable advantages to the application field of the motor servo system.
Compared with a motor servo system control scheme based on a model, an accurate motor servo system model is not needed, and the gradient descent method is adopted to more fully eliminate errors caused by unknown nonlinearity, so that the dynamic performance and tracking precision of the motor servo system are obviously improved.
The advantages of the present disclosure, as well as those of additional aspects, will be described in detail in the following detailed description of embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain and do not limit the disclosure.
FIG. 1 is a flow chart of a control method of embodiment 1 of the present disclosure;
FIG. 2 is a graph of tracking variation of the angle of rotation of a simulated example motor servo system of embodiment 1 of the present disclosure;
FIG. 3 is a graph of the variation of rotational angular velocity of a simulated example motor servo system of embodiment 1 of the present disclosure;
fig. 4 is a variation curve of a simulated example fuzzy base function estimate of embodiment 1 of the present disclosure.
Detailed Description
The disclosure is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof. It should be noted that, without conflict, the various embodiments and features of the embodiments in the present disclosure may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
The disclosure provides a distributed preset time control method of a motor servo system based on a gradient descent method, which aims to solve the problems of the traditional method and improve the control performance and stability of the motor servo system. According to the method, the precise control and the rapid convergence of the motor servo system can be realized by introducing a preset time concept and a fuzzy logic control strategy based on a gradient descent method and allowing independent parameters to freely pre-designate the convergence time. Compared with the traditional method, the distributed preset time fuzzy control method disclosed by the invention does not need accurate system model and parameter information, can fully eliminate the influence of non-linearity and uncertainty factors on system control, improves the robustness and adaptability of the system, has important application value in the field of motor servo systems, can effectively solve the challenges brought by non-linearity and uncertainty, improves the performance and precision of the motor servo system, and promotes the development of an automatic control technology. Specific examples are described below.
Example 1
In one or more embodiments, as shown in fig. 1 to 4, a motor control method based on a distributed preset time gradient descent method includes the following steps:
step 1, establishing a state space model of unknown nonlinearity aiming at a motor servo system to be controlled;
establishing a permanent magnet synchronous motor kinematic equation, and converting the permanent magnet synchronous motor kinematic equation into a corresponding state space model with unknown nonlinearity according to physical characteristics of the permanent magnet synchronous motor, namely obtaining a state space equation;
step 2, establishing a fuzzy logic approximation model, and determining the self-adaptive law of the fuzzy logic approximation model based on a gradient descent optimization algorithm;
the method comprises the steps of establishing a fuzzy logic approximation model for approximating nonlinear characteristics of a motor servo system;
step 3, constructing a preset time fuzzy controller based on the constructed state space model;
the preset time fuzzy controller comprises a virtual controller and an actual controller;
step 4, the unknown nonlinear characteristics of the motor servo system can be approximated by the fuzzy logic approximation model, learning factors in the self-adaptive law are adjusted based on the self-adaptive law, and a fuzzy basis function of the fuzzy logic approximation model is adaptively adjusted based on the acquired change of the operation data of the motor servo system;
and step 5, adjusting design parameters of the time fuzzy controller according to the obtained fuzzy base function and the learning factor to obtain convergence time of fuzzy control, and solving the state space model to obtain specific motor control actions.
Specifically, the adaptive law obtained according to step 2The learning factor kappa is adjusted, so that the model information of the fuzzy logic approximation model can be adjusted in a self-adaptive mode, and the fuzzy logic approximation model can approximate the unknown nonlinear function. Based on the virtual controller and the actual controller obtained in the step 3, design parameters in the controller are adjusted, so that the convergence time of the system can be set in advance, the stability of the system is ensured, and the quick response and accurate control on the system state are realized.
Further, the method also comprises the step 6: stability analysis and demonstration are carried out on the control system through the Lyapunov stability theory, so that the system is ensured to have stable performance under various working conditions, and convergence of preset time can be realized.
In this embodiment, the convergence time can be achieved irrespective of the initial conditions, and the convergence time is allowed to be freely specified by the independent parameters, so that accurate control of the motor servo system and rapid convergence of the preset time are achieved. The innovative method not only improves the dynamic performance and tracking precision of the system, but also enhances the adaptability of the system to uncertainty and nonlinear characteristics, and brings remarkable advantages to the application field of the motor servo system.
Compared with a motor servo system control scheme based on a model, an accurate motor servo system model is not needed, and the gradient descent method is adopted to more fully eliminate errors caused by unknown nonlinearity, so that the dynamic performance and tracking precision of the motor servo system are obviously improved.
The implementation process in step 1 may include the following processes:
step 1.1, according to the relation among the rotation angle, the rotation speed and the current of the motor, a permanent magnet synchronous motor motion model is established, and the equation is as follows:
wherein θ is a motor rotation angle, and ω represents a motor rotation speed. T (T) L For load torque, J represents moment of inertia, K t Is the motor torque constant, i q For motor q-axis current, B represents the coefficient of sliding friction.
Step 1.2, consider that the motor servo system may be affected by unknown nonlinearity under a complex operating environment. Let s 1 =θ and s 2 By transforming the motion model of the permanent magnet synchronous motor to obtain an unmodeled part of the motor servo system, the motion model of the permanent magnet synchronous motor is converted into a state space equation with unknown nonlinearity as follows:
wherein f=J -1 (-Bs 2 -T L ) The non-modeling part in the motor servo system is represented, namely the non-linear part. u=i q The input signal is the q-axis current of the motor, and the y represents the output signal is the rotation angle of the motor.
Considering the more general case, the motor servo system contains highly nonlinear characteristics, and all parameter information of the model cannot be determined in advance, as shown in formula (1.1).
In a further technical scheme, a fuzzy logic approximation model is constructed in the step 2, namely an unknown nonlinearity is approximated by adopting a fuzzy logic system; the fuzzy logic approximation model includes: a fuzzy rule base, a fuzzy inference engine, a fuzzifier and a defuzzifier.
In this embodiment, the fuzzy logic approximation model is also called a fuzzy logic system, and may be as follows:
wherein x= [ x ] 1 ,x 2 ,…,x n ] T And y is an input variable and an output variable of the fuzzy logic approximation model respectively;and G ι Respectively the variable x i And y, where i=1, 2, …, n.
Wherein,,representation->Membership function of>Represents G ι Membership functions of (a) are provided.
In this embodiment, the input variable of the fuzzy logic approximation model constructed for the motor servo system is the operation data of the motor servo system, and the operation data includes the rotation speed, angular velocity, etc. of the motor, which is s 1 =θ and s 2 =ω; the output variable is a fitting function which is not known to be nonlinear by the system, and the fitting function corresponds to the following formula (8).
The fuzzy basis function is selected as follows:
wherein an ideal weight vector is definedAnd basis function vectorCorresponding to iota=1, 2, … N, the fuzzy logic approximation model is the weight vector +.>And fuzzy basis function->Can be expressed as the product of:
as a special nonlinear function, the fuzzy logic approximation model can be used for approximation of the nonlinear function, so that the fuzzy logic approximation model is suitable for any nonlinear functionFor positive real numbers, there is a fuzzy logic approximation model +.>The following is established:
wherein Ω F Is thatA tight set of the above->For an n-dimensional real set, f (x) is defined as Ω F Given a continuous function thereon.
Secondly, the conventional fuzzy control algorithm has a plurality of defects when processing the unknown nonlinear function, for example, the fuzzy base function cannot be converged to an ideal value, so that a fuzzy logic system cannot work, namely the influence of the unknown nonlinearity on a motor servo system cannot be eliminated, and the system is possibly unstable and the working efficiency is possibly influenced.
According to a further technical scheme, based on a gradient descent method, a traditional fuzzy algorithm is improved, and the updating weight of a fuzzy base function is redesigned to obtain the updating law of the fuzzy base function, wherein the updating law is specifically as follows:
wherein, kappa is a learning parameter,zeta is the basis function vector T To estimate the weights, f (x) is defined as Ω F Given a continuous function, Ω F Is->A tight set of the above->Is n-dimensionalReal number set.
In this embodiment ζ * Represents ideal weight ζ T For estimating the weight, it is certainly best if the ideal weight can be achieved, which is not done in practice, so an estimated weight is designed.
The updating law has the function of allowing the fuzzy logic approximation model to adaptively adjust the fuzzy base function according to the change of input data, and improving the approximation capability and generalization capability of the system.
In step 3, a preset time fuzzy controller is constructed based on the constructed state space model, including the construction of a fuzzy rule based on a gradient descent method and the design of the preset time fuzzy controller.
The method for constructing the fuzzy controller of the preset time based on the self-adaptive law of the fuzzy logic approximation model obtained by the fuzzy rule of the gradient descent method comprises the following steps:
step 3.1, setting a preset time function, and designing a corresponding error conversion system based on the tracking reference signal;
definition y d The corresponding error conversion system is designed as follows by setting the tracking reference signal:
wherein omega 1 For tracking error omega 2 Is a virtual error, x 1 And x 2 Respectively input variable value alpha 1 Representing an output value of the virtual controller;
in this embodiment, η is a predetermined time function, satisfying η (0) =1 andits first derivative>The method meets the following conditions:
and->Is a positive constant;
L 1 ＝2+m，L 2 ＝1+m，L 3 =m and L 0 = 3+m, where m>0 is a positive number;
here, η is a function, and is not particularly limited to a function, so long as the above requirements are met, and may be called a predetermined time function, for example:t is preset convergence time, and the convergence time of the system can be adjusted by changing T.
Step 3.2, according to the fuzzy logic system constructed in the step 2, the fuzzy logic approximation model approximates the unknown nonlinearity of the motor servo system, and a fitting function of the unknown nonlinearity of the system can be obtained as follows:
wherein ζ * Represents ideal weight ζ *T Is the transpose of the ideal weights and,representing a basis function vector; delta e To approach error, satisfy +.>Is an arbitrarily small positive constant.
Step 3.3, for the motor rotation angle s in the state space equation 1 Deriving, based on the selected first Lyapunov function, a virtual controller;
selecting a suitable first lyapunov function as:
for the first Lyapunov function V 1 And (3) conducting derivation to obtain a corresponding virtual controller:
α 1 ＝-k 1 ηs 1 -y d (10)
wherein,,is a positive design parameter; l (L) 1 = 2+m, where m is a number greater than zero; y is d For the set tracking reference signal, eta is a preset time function;
step 3.4, for the motor rotation speed s in the state space equation 2 Deriving, based on the selected second Lyapunov function, a corresponding actual controller;
the appropriate lyapunov function was chosen as:
for V 1 The corresponding actual controller can be obtained by conducting derivation, and the specific form is as follows:
wherein omega 2 As a result of the virtual error,derivative of the virtual controller, lambda is a positive constant,/>Is an ideal weight ζ * Is a function of the estimate of (2).
Based on the gradient descent algorithm in the step 2, the self-adaptive law of the fuzzy logic approximation model is obtained, and the process is as follows:
(1) Designing a cost function related to a nonlinear function and a fuzzy logic approximation model, and proving that the cost function is a convex function;
(2) The Lyapunov function related to the weight of the fuzzy logic approximation model is constructed so that the derivative of the constructed Lyapunov function is less than 0, and the adaptive law of the fuzzy logic approximation model is designed based on the cost function.
The adaptive law is designed as follows:
wherein,,representing a positive design parameter. />Is an ideal weight ζ * Is a function of the estimate of (2). /> Filter->Where h is a positive design parameter and η is a predetermined time function.
According to the obtained self-adaptive law, the model of the fuzzy logic system can be adjusted to approach the unknown nonlinearity of the system, and the method comprises the following specific steps: the fuzzy logic approximation model is made to adaptively adjust the model information by adjusting the learning factors in the adaptive law until the fuzzy logic approximation model is able to approximate the above unknown nonlinear function.
The fuzzy basis function is given directly, and can be adaptively selected and adjusted through input data. The weight of the fuzzy logic approximation model is adaptively adjusted mainly through an adaptive law. The weight can be automatically adjusted by giving an adaptive law: the fuzzy logic approximation model is made to adaptively adjust the model information by adjusting the learning factors in the adaptive law until the fuzzy logic approximation model is able to approximate the above unknown nonlinear function.
In step 4, the convergence time can be formulated in advance by using the learning factor in the adaptive law as an independent parameter, and the convergence time can be realized without being limited by the initial condition.
According to the virtual controller alpha obtained in the step 3 1 The actual controller u can set the convergence time of the system in advance and ensure the stable operation of the system, and finally solves the state space model to obtain a specific motor control action, and the specific steps are as follows:
step 51, setting a form of a eta function which is a preset time function in the controller;
step 52, setting a preset time T according to the actual scene requirement, so as to determine the value of a preset time function eta and obtain a convergence time value;
step 53, during the convergence time, to enable the output y of the motor servo system to track the given reference signal y d To the aim, setting parameters of a fuzzy controller with preset time are adjusted based on the determined fuzzy base functionEnabling the output y of the motor servo system to track a given reference signal y d Obtaining a time fuzzy controller;
and step 54, solving the state space model based on the determined time fuzzy controller to obtain a specific motor control action.
In the embodiment, a fuzzy logic approximation model design scheme based on a gradient descent method is introduced so as to better adapt to the nonlinear characteristics and uncertainty of a motor servo system. Thanks to the gradient descent optimization algorithm, the system can effectively inhibit interference and noise in the system by automatically adjusting parameters of the fuzzy logic approximation model and parameters of the dynamic model, and improve the stability and control performance of the system.
Furthermore, stability analysis and demonstration are carried out on the control system through the Lyapunov stability theory, so that the system is ensured to have stable performance under various working conditions, and accurate convergence can be realized according to preset time requirements. And automatically adjusting parameters of the fuzzy logic approximation model and parameters of the dynamic model by combining tracking errors of a motor servo system through a distributed preset time algorithm based on the fuzzy logic approximation model.
In the embodiment, the method integrates the kinematics and dynamics modeling, the fuzzy logic control, the gradient descent method, the introduction of preset time and the stability analysis method of the motor system, and can realize the accurate control and the quick response of the motor servo system under the condition of not depending on an accurate model. The method has important research and application values for promoting the development of an automation control technology of a motor servo system and improving the efficiency and performance of industrial automation and robot application.
To demonstrate the effectiveness of this embodiment, the following simulation verification was performed:
in order to further illustrate the technical effects of the technical scheme, the simulation experiment verification process of the distributed preset time control method of the motor servo system based on the gradient descent method provided by the invention is as follows:
in the simulation experiment, the reference rotation angle of the motor is as follows: y is d =sin (0.5 t). According to the actual system, the system physical parameters in the model adopted in the example can be selected as T L ＝0.06125kg·m 2 B=0.008, the rest can be adjusted according to the actual situation. The initial state of the system is set as follows: s is(s) 1 (0) =0 and s 2 (0) =0. The initial state of the adaptive reference is set as:the control parameters are set as follows: k (k) 1 ＝5，k 2 ＝5，/>λ＝10，/>And t=1. MATLAB software is used for simulating the mathematical model established in the control method of the embodiment to obtain a simulation diagram2-fig. 4. FIG. 2 is a graph showing a tracking variation of a rotation angle of a motor servo system according to an embodiment; FIG. 3 is a graph showing a change in rotational angular velocity of a motor servo system according to an embodiment; fig. 4 is a graph showing the variation of the model basis function estimation in the embodiment. According to the simulation results, under the proposed distributed preset time fuzzy algorithm, the rotation angle of the motor servo system can quickly converge and track the upper reference track within preset time T=1s, and stable operation of the whole system is ensured.
Example 2
Based on embodiment 1, a motor control system based on a distributed preset time gradient descent method is provided in this embodiment, including:
a first construction module: the system is configured to establish a permanent magnet synchronous motor kinematic equation and convert the equation into a corresponding state space model with unknown nonlinearity;
and a second construction module: the method comprises the steps of being configured to establish a fuzzy logic approximation model and determining an adaptive law of the fuzzy logic approximation model based on a gradient descent optimization algorithm;
the fuzzy logic approximation model is used for approximating nonlinear characteristics of the motor servo system;
and a third construction module: the system comprises a state space model, a preset time fuzzy controller and a control unit, wherein the state space model is used for building the state space model;
and the self-adaptive adjustment module is used for: the fuzzy logic approximation model is configured to be capable of approximating unknown nonlinear characteristics of the motor servo system with the fuzzy logic approximation model, adjust learning factors in the adaptive law, and adaptively adjust a fuzzy basis function of the fuzzy logic approximation model based on changes of the acquired operation data of the motor servo system;
the control action output module: the method is configured to be used for adjusting design parameters of the time fuzzy controller according to the obtained fuzzy base function and the learning factor, obtaining convergence time of fuzzy control, and solving the state space model to obtain specific motor control actions.
Here, the modules in this embodiment are in one-to-one correspondence with the steps in embodiment 1, and the implementation process is the same, which is not described here.
Example 3
The present embodiment provides an electronic device including a memory, a processor, and computer instructions stored on the memory and running on the processor, where the computer instructions, when executed by the processor, perform the steps in the motor control method based on the distributed preset time gradient descent method described in embodiment 1.
Example 4
The present embodiment provides a computer-readable storage medium storing computer instructions that, when executed by a processor, perform the steps of the motor control method based on the distributed preset time gradient descent method described in embodiment 1.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.
Claims (10)
1. The motor control method based on the distributed preset time gradient descent method is characterized by comprising the following steps of:
establishing a permanent magnet synchronous motor kinematic equation, and converting the equation into a corresponding state space model with unknown nonlinearity;
establishing a fuzzy logic approximation model, and determining the self-adaptive law of the fuzzy logic approximation model based on a gradient descent optimization algorithm;
constructing a preset time fuzzy controller based on the constructed state space model;
the unknown nonlinear characteristics of the motor servo system can be approximated by the fuzzy logic approximation model, learning factors in the self-adaptive law are adjusted, and the fuzzy basis function of the fuzzy logic approximation model is self-adaptively adjusted based on the acquired change of the operation data of the motor servo system;
and according to the obtained fuzzy base function and the learning factor, adjusting design parameters of the time fuzzy controller to obtain convergence time of fuzzy control, and solving a state space model to obtain motor control actions.
2. The motor control method based on the distributed preset time gradient descent method as claimed in claim 1, wherein the construction method of the state space model comprises the following steps:
according to the relation among the rotating angle, the rotating speed and the current of the motor, a permanent magnet synchronous motor motion model is established;
and transforming the motion model of the permanent magnet synchronous motor to obtain an unmodeled part in the motor servo system, and converting the motion model of the permanent magnet synchronous motor into a state space equation with unknown nonlinearity.
3. The motor control method according to claim 1, wherein the fuzzy logic approximation model is a product of the update weight and the fuzzy basis function.
4. A motor control method based on a distributed preset time gradient descent method as claimed in claim 3, wherein: based on a gradient descent method, the update weight of the fuzzy base function is designed, and the update law of the fuzzy base function is obtained, specifically as follows:
wherein, kappa is a learning parameter,is a basis function vector>To estimate the weights, f (x) is defined as Ω F Given a continuous function, Ω F Is->A tight set of the above->Is an n-dimensional real set.
5. The motor control method based on the distributed preset time gradient descent method as claimed in claim 1, wherein: based on the gradient descent algorithm, the self-adaptive law of the fuzzy logic approximation model is obtained, and the process is as follows:
designing a cost function related to a nonlinear function and a fuzzy logic approximation model, and proving that the cost function is a convex function;
and constructing the Lyapunov function related to the update weight of the fuzzy base function in the fuzzy logic approximation model, so that the derivative of the constructed Lyapunov function is smaller than zero, and designing the self-adaptive law of the fuzzy logic approximation model based on the cost function.
6. The motor control method based on a distributed preset time gradient descent method as claimed in claim 1, wherein the method for constructing the preset time fuzzy controller comprises the steps of:
setting a preset time function, and designing a corresponding error conversion system based on the tracking reference signal;
approximating unknown nonlinearity of a motor servo system by adopting a fuzzy logic approximation model, and determining a fitting function of the unknown nonlinearity of the system;
deriving a motor rotation angle in a state space equation, and selecting a first Lyapunov function to obtain a virtual controller;
and deriving the motor rotation speed in the state space equation, and selecting a second Lyapunov function to obtain a corresponding actual controller.
7. The motor control method based on the distributed preset time gradient descent method according to claim 1, wherein determining preset time controller parameters and solving to obtain a motor control action comprises:
determining a form of a preset time function in a preset time controller;
setting preset time according to actual scene requirements, so as to determine the numerical value of a preset time function and obtain a convergence time value;
within the convergence time, so that the output y of the motor servo system can track the given reference signal y d Adjusting setting parameters of a preset time fuzzy controller based on the determined fuzzy base function to obtain the time fuzzy controller;
and solving the state space model based on the determined time fuzzy controller to obtain a specific motor control action.
8. The motor control system based on the distributed preset time gradient descent method is characterized by comprising the following components:
a first construction module: the system is configured to establish a permanent magnet synchronous motor kinematic equation and convert the equation into a corresponding state space model with unknown nonlinearity;
and a second construction module: the method comprises the steps of setting up a fuzzy logic approximation model, approximating nonlinear characteristics of a motor servo system, and determining an adaptive law of the fuzzy logic approximation model based on a gradient descent optimization algorithm;
and a third construction module: the system comprises a state space model, a preset time fuzzy controller and a control unit, wherein the state space model is used for building the state space model;
and the self-adaptive adjustment module is used for: the fuzzy logic approximation model is configured to be capable of approximating unknown nonlinear characteristics of the motor servo system with the fuzzy logic approximation model, adjust learning factors in the adaptive law, and adaptively adjust a fuzzy basis function of the fuzzy logic approximation model based on changes of the acquired operation data of the motor servo system;
the control action output module: the method is configured to be used for adjusting design parameters of the time fuzzy controller according to the obtained fuzzy base function and the learning factor, obtaining convergence time of fuzzy control, and solving the state space model to obtain specific motor control actions.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and running on the processor, which when executed by the processor, perform the steps of a method of controlling a motor based on a distributed pre-set time gradient descent method as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of a method of controlling a motor based on a distributed pre-set time gradient descent method as claimed in any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117311330A (en) * | 2023-11-29 | 2023-12-29 | 江西五十铃汽车有限公司 | Control method and system of whole vehicle controller, storage medium and electronic equipment |
| CN117478017A (en) * | 2023-12-27 | 2024-01-30 | 东莞市天一精密机电有限公司 | Control method of servo motor |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117311330A (en) * | 2023-11-29 | 2023-12-29 | 江西五十铃汽车有限公司 | Control method and system of whole vehicle controller, storage medium and electronic equipment |
| CN117311330B (en) * | 2023-11-29 | 2024-03-15 | 江西五十铃汽车有限公司 | Control method and system of whole vehicle controller, storage medium and electronic equipment |
| CN117478017A (en) * | 2023-12-27 | 2024-01-30 | 东莞市天一精密机电有限公司 | Control method of servo motor |
| CN117478017B (en) * | 2023-12-27 | 2024-04-09 | 东莞市天一精密机电有限公司 | Control method of servo motor |
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