High-precision voice coil motor position control system and method

Abstract

The invention discloses a high-precision voice coil motor position control system and method. Based on the working principle of the voice coil motor, a mathematical model is established for the voice coil motor according to an electric balance equation and a force balance equation, and a transfer function of the voice coil motor is obtained through various parameters of the voice coil motor. According to the transfer function, the designed control algorithm can adaptively adjust the PID parameter gain according to the feedback difference error and error change of the voice coil motor control system, the membership function of fuzzy control is improved, the control precision of the voice coil motor control system and the robustness of the system are improved, meanwhile, the RBF neural network is adopted as a PID gain compensator, and the robustness of the system is improved. Through on-line learning and adjustment, the fuzzy control and the output control parameter increment of the RBF neural network are combined by setting the weight, so that the defect that the traditional PID control is easily interfered by the environment is overcome, and the anti-interference capability and the stability of the system are enhanced.

Description

Technical Field

[0001] The invention belongs to the technical field of motors and relates to a high-precision voice coil motor position control system and method. Background Art

[0002] In some occasions where high-precision measurement and control of displacement and force are required, such as high-precision positioning systems such as lithography machines, digital cameras and hard disk drives, high-precision voice coil motors are usually used to achieve this. In voice coil motor control systems, traditional proportional integral differential control is usually used. This algorithm has the characteristics of high reliability and relatively mature development.

[0003] Traditional PID control can improve the dynamic and static performance of the system by adjusting the three parameters of the controller. However, in actual application scenarios, the control accuracy will be reduced due to external environmental interference factors and changes in the internal performance parameters of the voice coil motor. In addition, the PID controller has weak anti-interference ability and is not suitable for nonlinear systems.

[0004] The existing technology has made some optimization improvements to the traditional PID control, such as:

[0005] Prior art 1 proposes a dual closed-loop controller based on PD control, which enhances the anti-interference ability of the system and adopts feedforward error compensation to eliminate the feedforward position error caused by inaccurate frequency response data identification.

[0006] The second prior art proposes a voice coil motor position control system using position feedforward and position feedback PID control, and position feedforward and PID control are used for the voice coil motor position loop.

[0007] The third prior art adopts the PID control method to design a "proportional feedforward-feedback" composite controller, and uses MATLAB / Simulink to establish a system model for simulation analysis, which verifies that the vibration isolation of the Stewart platform designed by the third prior art has a good effect.

[0008] The fourth existing technology adopts fuzzy PID to control the voice coil motor. Through the step response performance experiment, it is found that the rise time, steady-state time and overshoot of fuzzy PID are better than those of traditional PID.

[0009] Prior art five proposes an intelligent control strategy based on neural network, uses S function to realize online adjustment of neural network, automatically adjusts parameters to find the best, and uses Simulink simulation to obtain a small tracking error of this algorithm, which verifies the feasibility of this algorithm.

[0010] In summary, high-precision control of voice coil motors faces the following problems in traditional control: 1. Timely adjustment of the current and position of the voice coil motor requires fast response, which places high demands on the hardware and control algorithm of the control system; 2. The force-current relationship of the voice coil motor can be approximated as a linear relationship, but air resistance, guide rail friction and other mechanical resistances will introduce nonlinear factors. The impact of these factors needs to be considered to improve control accuracy, but traditional PID control cannot effectively eliminate these errors. Summary of the invention

[0011] In view of the above background problems, the present invention proposes a high-precision voice coil motor position control system and method, the purpose of which is to improve the control accuracy of the voice coil motor and the stability of the system when the voice coil motor realizes high-precision position control. The present invention can solve the above problems faced by traditional control.

[0012] On the one hand, the present invention provides a high-precision voice coil motor position control system, comprising: a voice coil motor, a grating ruler, a control chip and a driver;

[0013] According to the preset displacement information of the voice coil motor, the voice coil motor is controlled through the control chip and the driver; the grating ruler detects the actual displacement information of the voice coil motor and feeds it back to the control chip, and a new control quantity is obtained after calculation and processing. The control chip establishes communication with the driver, and the driver applies the driving signal to the voice coil motor to achieve high-precision control of the position of the voice coil motor.

[0014] Furthermore, the voice coil motor position control system also includes a host computer, and the host computer is used to generate a control algorithm.

[0015] Based on the working principle of the voice coil motor, a mathematical model of the voice coil motor is established according to the electric balance equation and the force balance equation, and its transfer function is obtained from various parameters of the voice coil motor; the control algorithm adopts a fuzzy PID controller based on the RBF neural network according to the transfer function, converts the displacement tracking problem of the voice coil motor into a tracking control problem of the PID position control loop, and uses the fuzzy control theory to perform real-time adaptive adjustment on the PID parameter gain.

[0016] In a preferred embodiment, the control algorithm includes a fuzzy controller, a RBF neural network and a PID controller.

[0017] The fuzzy controller generates a control parameter increment, the RBF neural network generates a compensation gain, the weight calculations of the fuzzy controller and the RBF neural network are integrated to obtain a PID control parameter increment, and then the PID control parameter increment acts on the PID controller.

[0018] Further, the control algorithm performs the following operations:

[0019] According to the comparison between the position information fed back by the voice coil motor position control system and the expected output position information, the deviation at the current moment is calculated, and the parameter gain of the control system in the initial stage is output through the fuzzy controller.

[0020] The control quantity of the PID controller gain in the initial stage and the feedback value of the voice coil motor position control system are introduced into the RBF neural network. The nonlinear system is infinitely approximated and autonomously learned through radial basis functions, and the sensitivity information Jacobian matrix is ​​output. The self-learning module is used to further fit the PID parameters of the input controller to obtain the compensation gain.

[0021] The control parameter increment of the PID controller is obtained from the weights of the fuzzy controller and the RBF neural network.

[0022] Furthermore, the fuzzy controller performs the following operations:

[0023] The deviation and the rate of change of the deviation are taken as input variables; the membership function and fuzzy rules are selected; fuzzy reasoning is performed according to the fuzzy rules, the membership is divided in the quantization interval of the deviation, and fuzzification processing and analysis are performed according to the fuzzy rules; defuzzification is performed to obtain the proportional increment, integral increment and differential increment values ​​output by the fuzzy controller.

[0024] Furthermore, the fuzzy controller designs the membership function of the fuzzy PID controller by combining a Gaussian membership function with a triangular membership function. Specifically, a low-resolution Gaussian membership function is selected for a large error area to provide a smooth control output; a high-resolution triangular membership function is selected for a small error area to avoid under-regulation or over-regulation.

[0025] Furthermore, the defuzzification adopts the centroid method, by calculating the area enclosed by the membership function curve and taking the horizontal axis as the axis, the centroid position of the current closed area is calculated, and the position coordinate is the final output value of the fuzzy controller.

[0026] Furthermore, the RBF neural network uses a three-layer feedforward network structure;

[0027] The first layer is the input layer composed of signal source nodes, the second layer is the hidden space composed of transformed radial basis functions, and the third layer is the network output, which is linearly weighted and summed with the network weight coefficient.

[0028] On the other hand, the present invention provides a high-precision voice coil motor position control method, which is applied to the above-mentioned high-precision voice coil motor position control system, and comprises the following steps:

[0029] S1, calculating the required incremental displacement value according to the displacement deviation of the voice coil motor;

[0030] S2, obtaining controller parameters according to the incremental displacement value, and calculating the output control signal; calculating the parameter increment of the PID controller according to the incremental displacement value, obtaining new controller parameters by the original controller parameters and the obtained parameter increment, and obtaining the output control signal;

[0031] S3. Control the voice coil motor to operate according to the control signal.

[0032] Further, the displacement deviation acquisition of the voice coil motor includes the following steps:

[0033] The target displacement of the voice coil motor is obtained; the current actual displacement of the voice coil motor is obtained; and the displacement deviation is obtained according to the target displacement and the actual displacement.

[0034] The technical effects produced by the present invention are:

[0035] The control algorithm designed in the present invention can adaptively adjust the PID parameter gain according to the feedback difference error and error change of the voice coil motor control system, improve the membership function of fuzzy control, improve the control accuracy of the voice coil motor control system and the robustness of the system, and adopt RBF neural network as PID gain compensator. Through online learning and adjustment, weights are set to combine the output control parameter increment of fuzzy control and RBF neural network, thereby overcoming the disadvantage that traditional PID control is easily affected by environmental interference and enhancing the anti-interference ability and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is an overall structural diagram of the voice coil motor position control system according to an embodiment of the present application.

[0037] Figure 2 This is a structural diagram of the control algorithm (i.e., fuzzy RBF neural network PID control) of an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] The present invention aims to improve the stability and control accuracy of the system while being able to adaptively adjust the parameters of the PID controller, and to provide effective technical support for the research on high-precision voice coil motor position control. Since the voice coil motor will be affected by nonlinear factors during operation, a PID control method based on a fuzzy RBF neural network is proposed to achieve adaptive tuning of the PID parameters, overcome the shortcomings of traditional PID parameters that rely on experience and cannot be adjusted in real time according to system changes, and reduce the interference to the system caused by environmental influences through online learning and adjustment of the RBF neural network, reduce the nonlinear error generated by the voice coil motor during the control process, and improve the control accuracy and system stability of the voice coil motor position control system.

[0040] The basic concept of the present invention is as follows: Based on the working principle of the voice coil motor, the present invention establishes a mathematical model for the voice coil motor according to the electrical balance equation and the force balance equation, and obtains its transfer function from various parameters of the voice coil motor. According to the transfer function, a PID controller is designed to control the position loop of the voice coil motor, and on the basis of the PID controller, the fuzzy control theory and the RBF neural network are combined to optimize the traditional control algorithm of the voice coil motor, realize the real-time adaptive adjustment of the PID parameter gain and reduce the interference of the environmental influence on the system.

[0041] The implementation steps of the present invention are further described below with reference to the accompanying drawings and specific examples:

[0042] The present application embodiment provides a high-precision voice coil motor position control system (hereinafter referred to as the system), which is specifically implemented as follows:

[0043] 1. System composition and basic working principle

[0044] like Figure 1 As shown, the voice coil motor control system mainly includes: voice coil motor, grating ruler, control chip, driver and host (PC) computer. The host computer is used to generate PID control algorithm based on fuzzy RBF neural network.

[0045] (1) Working principle of voice coil motor:

[0046] The voice coil motor achieves motion control through the Lorentz force generated by the current in the magnetic field. The core is to generate linear motion in the magnetic field by controlling the magnitude and direction of the current. When the current I passes through the coil and the coil is located in a region perpendicular to the magnetic field B, according to the Lorentz force principle, a thrust F perpendicular to the current direction will be generated. Where F is the thrust generated, B is the magnetic field strength, I is the current in the coil, L is the effective length of the coil, and θ is the angle between the current direction and the magnetic field direction.

[0047] By controlling the magnitude and direction of the current, the mover can be made to reciprocate in a certain direction, and by constantly switching the direction of the current, the voice coil motor can be made to reciprocate. The force and current are linearly related. When a constant current is given, a stable thrust can be output. When the current changes with time, dynamic thrust changes can be achieved, which can be used for position or speed control.

[0048] (2) Basic working methods of the system:

[0049] According to the preset displacement information of the voice coil motor, the voice coil motor is controlled through the control chip and the driver. The actual displacement information of the voice coil motor is detected by the grating ruler and fed back to the ZYNQ-7020 control chip. After error calculation and calculation and processing by the designed fuzzy RBF neural network PID control algorithm, a new control quantity is obtained to achieve high-precision control of the voice coil motor position.

[0050] According to the working principle of the voice coil motor, its electrical balance equation and force balance equation can be listed.

[0051] From the electromagnetic force equation we get:

[0052]

[0053] Among them, B is the magnetic induction intensity in the space where the voice coil is located, I is the current flowing through the voice coil conductor, and L is the effective length of the voice coil conductor in the magnetic field.

[0054] When direct current passes through the coil, a magnetic field force based on the current is generated in the coil, and an induced back electromotive force E is also generated. a , the magnitude of the induced back electromotive force is expressed by the following formula:

[0055]

[0056] Among them, E a is the induced back electromotive force, BL is the motor force constant, and v is the speed at which the armature cuts the magnetic flux lines.

[0057] Assume that the armature terminal voltage of the voice coil motor is U i , I is the current flowing through the coil, the total resistance of the drive circuit is R, and Kirchhoff's law gives the following formula:

[0058]

[0059] Among them, L a is the coil inductance, and the motor force constant is K f , from the above formula we can get:

[0060]

[0061] From the force balance equation, we can get the electromagnetic force F e for:

[0062]

[0063] Where k is the friction coefficient and m is the mass of the coil.

[0064] From the above equation, eliminate the intermediate variables I and F e And through Laplace transform, the transfer function between voltage u and voice coil motor displacement x can be obtained:

[0065]

[0066] 2. Specific implementation of control algorithm

[0067] like Figure 2 As shown in FIG. 1 , the control algorithm realizes the self-tuning of PID parameters through fuzzy controller, RBF neural network and PID controller. Where e is the difference between the measured value and the given value, e c is the rate of change of the deviation, x set is the voice coil motor position control setting value, x out is the voice coil motor position output value, x m is the output displacement value of the third layer of the neural network, γ is the network recognition performance index function, ξ jacobin is the Jacobian matrix identification, u(t) is the voltage control quantity output by PID to the voice coil motor, λ and β are the weight coefficients of the fuzzy controller and the RBF neural network, K p0 ,K i0 ,K d0 is the PID parameter increment output by the fuzzy controller, K p1 ,K i1 ,K d1 It is the PID parameter increment output by the RBF neural network.

[0068] Fuzzy reasoning is performed based on the error and the rate of change of the error to obtain the proportional increment △Kp, integral increment △Ki and differential increment △Kd of the fuzzy PID controller output, and combined with PID to form new control parameters to achieve precise control of the system.

[0069] The working method of the fuzzy controller is: first, the input signal is fuzzified, then fuzzy reasoning is performed according to the set fuzzy rules, and finally the control quantity is obtained by defuzzification. The fuzzy controller can adjust and update the parameters of the PID in real time, effectively improving the anti-interference ability of the system.

[0070] According to the fuzzy control principle, the input variables e and e c Fuzzy processing is performed, where e is the difference between the measured value and the given value, e c is the rate of change of the deviation.

[0071] The membership functions of proportion, integration, and differentiation are set to Gaussian functions, and the membership functions of error and error rate of change are set to the combination of Gaussian and triangular functions. The variable fuzzy subsets are set to determine e and e c The domain of the word is divided into seven parts according to {NB, NM, NS, ZO, PS, PM, PB}. N stands for negative, P stands for positive, ZO stands for zero, B stands for big, M stands for medium, and S stands for small. That is, PB stands for positive big, PS stands for positive small, and the same applies to the others.

[0072] The selection of fuzzy rules and membership functions is related to the performance of the fuzzy PID controller. The selection of the membership function in the embodiment of the present application is determined after multiple simulation tests, and the fuzzy rules determined by expert experience are selected to provide relatively stable control performance.

[0073] The embodiment of the present application adopts a Gaussian membership function combined with a triangular membership function to design the membership function of the fuzzy PID controller. Specifically, a low-resolution Gaussian membership function is selected for a larger error area to provide a smooth control output in the large error area. The curve of the Gaussian membership function is very smooth, which helps to reduce the jitter and violent response of the system in the large error area; a high-resolution triangular membership function is selected for a smaller error area. When processing small errors, the high-resolution triangular membership function can finely adjust the controller output when the error is close to zero, avoid under-adjustment or over-adjustment, and ensure that the system has higher accuracy when approaching the target state.

[0074] The form of each fuzzy rule is: If e(t) is…and e c (t) is…, then k p0 is…,k i0 is…,k d0 is….

[0075] According to the reasoning of Mamdani's law, the membership degree is divided in the quantization interval of the deviation, and the fuzzy processing and analysis are carried out according to the fuzzy control rule table, and then the values ​​of △Kp, △Ki and △Kd are obtained by defuzzification through the centroid rule.

[0076] The defuzzification method uses the centroid method. By calculating the area enclosed by the membership function curve and taking the horizontal axis as the axis, the centroid position of the current closed area is calculated. The position coordinate is the final output value of the fuzzy controller, and the expression is:

[0077]

[0078] In this process, in order to map the fuzzy domain to the actual domain, a defuzzification factor is required , assuming that the actual domain is [-q, q] and the fuzzy domain is [-p, p], then the defuzzification factor is expressed as By introducing the defuzzification factor and mapping the fuzzy value to the actual value, the control parameter increment K can be obtained. p0 (t), K i0 (t) and K d0 (t).

[0079] Considering that when the system is suddenly disturbed by the environment, when the deviation input exceeds the domain set by the fuzzy controller, the fuzzy controller cannot provide appropriate gain parameters for the controlled system. Once the disturbance causes the deviation to exceed the effective control range, the control effect will be greatly reduced.

[0080] To this end, the powerful learning ability of the RBF neural network is utilized, and the anti-interference ability of the system is enhanced through online learning and adjustment of the neural network to ensure that the system can achieve position control smoothly and quickly. At the same time, the RBF neural network has strong robustness and fault tolerance, and can adjust and adapt to changes in the control system in a timely manner, so that the voice coil motor can better adapt to different working environments and have better stability when dealing with interference.

[0081] The RBF neural network used in the embodiment of the present application is a three-layer feedforward network structure:

[0082] The first layer is the signal source node The input layer consists of the second layer which is a transformation radial basis function The hidden space composed of the third layer is the network output x m , linearly weight it, using the network weight coefficient Sum.

[0083] Design of RBF neural network:

[0084] First, the data is nonlinearly mapped. When the input data enters the network, the RBF neural network nonlinearly maps the input data through the radial basis function. The output value of each hidden node represents the distance between the input vector and the center of the node. The RBF neural network needs to solve three parameters: the center of the radial basis function, the basis width, and the weight from the hidden layer to the output layer.

[0085] The Gaussian kernel function is used as the basis function of the RBF neural network. The Gaussian kernel function is shown as follows:

[0086]

[0087] Among them, b j is the basis width vector of the jth node, c j Represents the center vector of the jth node, j=1,2,…m.

[0088] According to the weight coefficient of the RBF neural network, the neural network output x can be calculated m and the network identification performance indicator function γ.

[0089]

[0090]

[0091] Among them, x set represents the setting of the target displacement of the voice coil motor, x out Indicates the position information of the voice coil motor, h j is the output of the jth neuron in the hidden layer. In order to achieve the best control effect, according to the gradient descent method, the performance index searches for the optimal solution along the negative gradient direction and updates the node center vector c through iteration. j , basis width vector b j , network weight coefficient ω j The sensitivity information is identified by the Jacobian matrix, the network identification index is updated, and the merit of the network identification index is obtained.

[0092] The neural network updates the above parameters through the following formula, where μ represents the network learning efficiency and α represents the momentum factor.

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] Among them, w j is the weight coefficient from the hidden layer to the output layer, xset is the voice coil motor position control setting value, x out is the voice coil motor position output value, x m is the output displacement value of the third layer of the neural network, γ is the network recognition performance index function, ξ jacobin is the Jacobian matrix identification, u(t) is the voltage control quantity output by PID to the voice coil motor, λ and β are the weight coefficients of the fuzzy controller and the RBF neural network, K p0 ,K i0 ,K d0 is the PID parameter increment output by the fuzzy controller, K p1 ,K i1 ,K d1 It is the PID parameter increment output by the RBF neural network.

[0101] According to the system structure, the "5-8-4" RBF neural network structure is used to adjust the control gain. From the following formula, we can see that the network structure input is the PID control three-phase input X in (1),X in (2),X in (3) The deviation e(t) of the controlled system and the actual position information feedback x out , the fuzzy RBF neural network PID control structure diagram is as shown in the attached Figure 2 shown.

[0102] As shown in equations 1-18 and 1-19, the control parameter K is adjusted using the gradient descent method. p1 (t),K i1 (t),K d1 (t), where η p ,η i ,η d Represent the PID gain learning rate respectively.

[0103]

[0104]

[0105] According to the comparison between the position information fed back by the system and the position information expected to be output, the deviation e at the current moment is calculated. After fuzzification, fuzzy reasoning and defuzzification of the fuzzy control system, the K of the control system in the initial stage is output. p0 (t),K i0 (t),K d0 (t) gain. Then, the control quantity u(t) of the PID controller gain in the initial stage and the system feedback value x outThe RBF neural network is introduced to output the Jacobian matrix of the sensitivity information of the controlled system's control quantity to the change of the controller input through the infinite approximation and autonomous learning of the nonlinear system by radial basis function.

[0106] Finally, the self-learning module is used to further fit the PID parameters of the input controller to obtain the compensation gain K p1 (t),K i1 (t),K d1 (t).

[0107] The PID control parameter increment can be obtained from the weights of the fuzzy controller and the neural network. Therefore, the PID control parameter increment is λK p0 (t)+βK p1 (t),λK i0 (t)+βK i1 (t),λK d0 (t)+βK d1 (t), to achieve real-time adaptive adjustment of PID parameter gains and reduce the interference of environmental influences on the system, where: .

[0108] The embodiment of the present application further provides a high-precision voice coil motor position control method, which is applied to the high-precision voice coil motor position control system described above, and includes the following steps:

[0109] S1. Calculate the required incremental displacement value based on the displacement deviation of the voice coil motor.

[0110] S2. Obtain controller parameters according to the incremental displacement value, and calculate the output control signal; calculate the parameter increment of the PID controller according to the incremental displacement value, obtain new controller parameters from the original controller parameters and the obtained parameter increment, and obtain the output control signal.

[0111] S3. Control the voice coil motor to operate according to the control signal.

[0112] Wherein, the displacement deviation acquisition of the voice coil motor comprises the following steps:

[0113] The target displacement of the voice coil motor is obtained; the current actual displacement of the voice coil motor is obtained; and the displacement deviation is obtained according to the target displacement and the actual displacement.

[0114] In summary, the present application is highly professional for high-precision control of voice coil motors. It is difficult to quickly and accurately achieve the control purpose using traditional PID controllers, and it is difficult to adaptively adjust the controller parameters, and it is difficult to respond and adjust in time when the system is disturbed by external interference. The present invention optimizes the controller, which can not only achieve adaptive adjustment of PID controller parameters in high-precision control of voice coil motors, but also achieve rapid response, better anti-interference performance, and improve the control accuracy of voice coil motors. It can be used as an alternative and beneficial supplement to voice coil motor control methods.

[0115] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-precision voice coil motor position control system, characterized in that: include: Voice coil motor, grating ruler, control chip and driver; According to the preset displacement information of the voice coil motor, the voice coil motor is controlled through the control chip and the driver; the grating ruler detects the actual displacement information of the voice coil motor and feeds it back to the control chip, and a new control quantity is obtained after calculation and processing. The control chip establishes communication with the driver, and the driver applies the driving signal to the voice coil motor to achieve high-precision control of the position of the voice coil motor.

2. A high-precision voice coil motor position control system according to claim 1, characterized in that: It also includes the host computer; The host computer is used to generate a control algorithm; Based on the working principle of the voice coil motor, a mathematical model of the voice coil motor is established according to the electrical balance equation and the force balance equation, and its transfer function is obtained from various parameters of the voice coil motor; The control algorithm adopts a fuzzy PID controller based on RBF neural network according to the transfer function, transforms the displacement tracking problem of the voice coil motor into a tracking control problem of the PID position control loop, and uses fuzzy control theory to perform real-time adaptive adjustment on the PID parameter gain.

3. A high-precision voice coil motor position control system according to claim 2, characterized in that: The control algorithm includes a fuzzy controller, an RBF neural network and a PID controller; The fuzzy controller generates a control parameter increment, the RBF neural network generates a compensation gain, the weight calculations of the fuzzy controller and the RBF neural network are integrated to obtain a PID control parameter increment, and then the PID control parameter increment acts on the PID controller.

4. A high-precision voice coil motor position control system according to claim 3, characterized in that: The control algorithm performs the following operations: According to the comparison between the position information fed back by the voice coil motor position control system and the expected output position information, the deviation at the current moment is calculated, and the parameter gain of the control system in the initial stage is output through the fuzzy controller; The control quantity of the PID controller gain in the initial stage and the feedback value of the voice coil motor position control system are introduced into the RBF neural network. The nonlinear system is infinitely approximated and autonomously learned through radial basis functions, and the sensitivity information Jacobian matrix is ​​output. The PID parameters of the input controller are further fitted using the self-learning module to obtain the compensation gain. The control parameter increment of the PID controller is obtained from the weights of the fuzzy controller and the RBF neural network.

5. A high-precision voice coil motor position control system according to claim 3 or 4, characterized in that: The fuzzy controller performs the following operations: The deviation and the rate of change of the deviation are taken as input variables; the membership function and fuzzy rules are selected; fuzzy reasoning is performed according to the fuzzy rules, the membership is divided in the quantization interval of the deviation, and fuzzification processing and analysis are performed according to the fuzzy rules; defuzzification is performed to obtain the proportional increment, integral increment and differential increment values ​​output by the fuzzy controller.

6. A high-precision voice coil motor position control system according to claim 5, characterized in that: The fuzzy controller adopts a Gaussian membership function combined with a triangular membership function to design the membership function of the fuzzy PID controller, specifically: a low-resolution Gaussian membership function is selected for a large error area to provide a smooth control output; a high-resolution triangular membership function is selected for a small error area to avoid under-regulation or over-regulation.

7. A high-precision voice coil motor position control system according to claim 5, characterized in that: The defuzzification adopts the centroid method, by calculating the area enclosed by the membership function curve and taking the horizontal axis as the axis, the centroid position of the current closed area is calculated, and the position coordinate is the final output value of the fuzzy controller.

8. A high-precision voice coil motor position control system according to claim 3, characterized in that: The RBF neural network adopts a three-layer feedforward network structure; The first layer is the input layer composed of signal source nodes; the second layer is the hidden space composed of transformed radial basis functions; the third layer is the network output, which is linearly weighted and summed with the network weight coefficients.

9. A high-precision voice coil motor position control method, applied to a high-precision voice coil motor position control system as claimed in any one of claims 1 to 8, characterized in that: The following steps are involved: S1, calculating the required incremental displacement value according to the displacement deviation of the voice coil motor; S2, obtaining controller parameters according to the incremental displacement value, and calculating the output control signal; calculating the parameter increment of the PID controller according to the incremental displacement value, obtaining new controller parameters by the original controller parameters and the obtained parameter increment, and obtaining the output control signal; S3. Control the voice coil motor to operate according to the control signal.

10. A high-precision voice coil motor position control method according to claim 9, characterized in that: The displacement deviation acquisition of the voice coil motor comprises the following steps: The target displacement of the voice coil motor is obtained; the current actual displacement of the voice coil motor is obtained; and the displacement deviation is obtained according to the target displacement and the actual displacement.

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