Voice coil motor fast reflecting mirror control method based on preset performance control

Abstract

The invention provides a voice coil motor fast reflecting mirror control method based on preset performance control, and the method specifically comprises the steps: building a voice coil motor fast reflecting mirror mathematical model, and correcting the voice coil motor fast reflecting mirror mathematical model in consideration of internal and external unknown interference variables; designing a preset performance controller by combining a time-varying tangent obstacle Lyapunov function and a neural network according to the voice coil motor fast reflecting mirror mathematical model, deriving a voice coil motor fast reflecting mirror control law and a neural network weight adaptive law, and fitting internal and external unknown disturbances by the neural network; stability analysis is carried out on the derived voice coil motor fast reflecting mirror control law, so that all closed-loop signals of the system are consistent and finally bounded. According to the method, the tracking precision of the fast steering mirror can be preset, so that output behaviors such as overshoot and steady-state errors of the system are ensured to be within a preset performance boundary, the transient and steady-state performance of the system is effectively improved, and the anti-interference capability of the system is improved in combination with the neural network.

Description

Technical Field

[0001] This invention relates to the field of high-precision photoelectric tracking system technology, and in particular to a voice coil motor fast-reflecting mirror control method based on preset performance control. Background Technology

[0002] Photoelectric tracking systems are widely used in high-precision tracking fields such as astronomical observation. In recent years, with the increasing demands of applications, new requirements have been placed on the tracking accuracy of photoelectric tracking systems. As the core component of a photoelectric tracking system, the voice coil motor fast reflector directly determines the final tracking performance of the system. Traditional control methods struggle to balance small overshoot with high tracking accuracy, and external interference from complex environments further reduces the system's tracking accuracy and stability. Therefore, improving the control capability of the voice coil motor fast reflector under internal and external interference is of great significance for enhancing the tracking performance of photoelectric tracking systems.

[0003] Application publication number CN 115424391 A proposes a backstepping sliding mode control method for a photoelectric tracking turntable, combining backstepping control and sliding mode control. This method effectively improves the anti-interference capability of the turntable. However, this scheme does not approximate the interference; instead, it relies on the robustness of the backstepping sliding mode control method itself to suppress the interference, resulting in limited actual anti-interference capability. The literature "Pre-correction control of a voice coil motor fast-reflecting mirror in a photoelectric precision tracking system" introduces position feedforward control based on PID control, effectively improving the tracking accuracy of the controller. However, its control over position control performance relies solely on the a posteriori setting of control parameters, making it impossible to pre-design the level of position tracking accuracy.

[0004] Preset performance control was first proposed by Bechlioulis in 2008. Preset performance control directly specifies the output behavior of the system through a preset performance function, so that the system tracking error output behavior remains within the preset performance boundary steady-state error boundary value from the initial transient to the final steady state. It has shown advantages in many high-precision control fields (such as UAV control and robot control), but it has not yet been applied to improve the tracking performance of voice coil motor fast reflectors. Summary of the Invention

[0005] Based on the above, the purpose of this invention is to provide a voice coil motor fast mirror control method that combines preset performance control with neural network control. This method enables the preset setting of the tracking accuracy of the fast mirror, allowing the system to maintain high tracking accuracy while having a small overshoot, thus effectively improving the transient and steady-state performance of the system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A fast-reflecting mirror control method for a voice coil motor based on preset performance control specifically includes the following steps:

[0008] A mathematical model of a voice coil motor fast-reflecting mirror is established, and the mathematical model is corrected by incorporating unknown internal and external disturbances.

[0009] Based on the mathematical model of the voice coil motor fast mirror, and combined with the time-varying tangent barrier Lyapunov function and neural network, a preset performance controller is designed, and the control law of the voice coil motor fast mirror and the adaptive law of neural network weights are derived. The neural network fits the unknown internal and external disturbances.

[0010] Stability analysis was performed on the derived voice coil motor fast mirror control law.

[0011] As a preferred embodiment of the voice coil motor fast-reflecting mirror control method based on preset performance control, the establishment of a mathematical model of the voice coil motor fast-reflecting mirror and the correction of the mathematical model by incorporating unknown internal and external disturbances specifically include:

[0012] Based on the electrical characteristics of the voice coil motor, the voltage balance equation for the fast-reflecting mirror can be obtained as follows:

[0013]

[0014] in, For armature resistance, For armature current, For armature inductance, The back electromotive force coefficient, For the voice coil motor rotation angle, Armature input voltage;

[0015] Based on the mechanical characteristics of the voice coil motor, the torque balance equation for the fast-reflecting mirror can be obtained as follows:

[0016]

[0017] in, The moment of inertia of the motor. This is the viscous damping coefficient of the motor. The elastic coefficient of the flexible hinge. The electromagnetic torque constant is The angular velocity of the motor. This refers to the angular acceleration of the motor.

[0018] Combining equations (1) and (2) and performing a Laplace transform, we obtain:

[0019]

[0020] Eliminate intermediate variables The mathematical model of the fast-reflecting mirror of the voice coil motor can be obtained as follows:

[0021]

[0022] The armature inductance The error is very small and negligible, and within the bandwidth of the fast-reflecting mirror, the fitting error of the second-order and third-order mathematical models of the fast-reflecting mirror is similar. Therefore, equation (4) can be simplified to:

[0023]

[0024] In the formula, , , ;

[0025] Equation (5) can be written in the form of a differential equation:

[0026]

[0027] Considering that the fast-reflecting mirror will be subject to internal and external interference in practical applications, the mathematical model of the voice coil motor fast-reflecting mirror can be modified as follows:

[0028]

[0029] In the formula, The unknown internal and external disturbances experienced by the system.

[0030] As a preferred embodiment of the voice coil motor fast-reflecting mirror control method based on preset performance control, the preset performance controller is designed according to the mathematical model of the voice coil motor fast-reflecting mirror, combined with Lyapunov functions and neural networks, and the control law of the voice coil motor fast-reflecting mirror and the adaptive law of neural network weights are derived. The adaptive law of neural network weights fits the unknown internal and external disturbances, specifically including:

[0031] Equation (7) can be written in the form of the following state-space equation:

[0032]

[0033] in, , The error variable is defined as:

[0034]

[0035] in, To give the position of the fast-reflecting mirror, For virtual control;

[0036] To ensure that the position output error always remains within the preset performance boundaries, the following preset performance function is constructed:

[0037]

[0038] in, To preset the initial performance values, To preset the performance steady-state error boundary value, , This is a positive constant used to adjust the preset performance convergence speed;

[0039] Define the first Lyapunov function as:

[0040]

[0041] Differentiating equation (11) yields:

[0042]

[0043] Substituting equation (9) into equation (12), we get:

[0044]

[0045] Design a virtual control law based on equation (13). for:

[0046]

[0047] in For the normal values ​​to be designed in the controller, the virtual control law is... Substituting into equation (13), we get:

[0048]

[0049] In addition, a sliding surface is introduced:

[0050]

[0051] in Let the positive constant be the second Lyapunov function, and define it as follows:

[0052]

[0053] in A constant greater than 0 For the ideal weights of the neural network, For the estimated weights of the neural network, To estimate the weights of the neural network, we can differentiate equation (17) to obtain:

[0054]

[0055] Substituting the mathematical model (7) of the voice coil motor fast-reflecting mirror into equation (18), we get:

[0056]

[0057] Due to the amount of interference The magnitude is unknown; here, an RBF neural network is used to measure the disturbance amount. For overall approximation, the following definition is given based on neural networks:

[0058]

[0059]

[0060] in Let be the radial basis function, and let be the neural network input. , For neural network approximation error, To approximate the upper bound of the error, Let be the coordinate vector of the center point of the radial basis functions. Given the width of the radial basis functions, rewriting equation (19) yields:

[0061]

[0062] The control law for the output voltage of the voice coil motor fast-reflecting mirror, based on equation (22), is as follows:

[0063]

[0064] in, For the positive constants to be designed for the controller, For robustness, For positive numbers, take... Substituting equation (23) into equation (22) yields:

[0065]

[0066] And because when hour, ;

[0067] when hour, Therefore, equation (24) becomes:

[0068]

[0069] in Based on the above formula, design the neural network weight adaptive law. for:

[0070]

[0071] in, , Substituting equation (26) into equation (25) for the controller parameters, we get:

[0072] .

[0074] As a preferred embodiment of the voice coil motor fast-reflecting mirror control method based on preset performance control, the stability analysis of the derived voice coil motor fast-reflecting mirror control law specifically includes:

[0075] It can be rewritten as:

[0076]

[0077] According to Young's inequality, we can obtain:

[0078]

[0079] therefore:

[0080]

[0081] The system selects the Lyapunov function. Substituting equation (31) into equation (27) yields:

[0082]

[0083] in, , The expressions are as follows:

[0084]

[0085]

[0086] By Lyapunov's stability theorem and As can be seen from the expression, choosing appropriate parameters makes , , , , Both are bounded, since a given position of a fast-reflecting mirror and its derivative are also bounded, by... , The expression shows that the system's state variables are also bounded. From the virtual control law (14), the voice coil motor fast mirror output voltage control law (23), and the neural network weight adaptive law (26), it can be seen that they are also bounded. Therefore, all closed-loop signals of the system are consistent and ultimately bounded.

[0087] As a preferred embodiment of the voice coil motor fast-reflection mirror control method based on preset performance control, an improved preset performance function is constructed to replace the preset performance function (10). The expression of the improved preset performance function is as follows:

[0088]

[0089] in, To preset the initial performance values, To preset the steady-state error boundary value, take , This represents the convergence speed. For the maximum convergence time, we can derive it from equation (35). , , It decreases strictly and monotonically over time.

[0090] As a preferred embodiment of the fast-reflecting mirror control method for voice coil motors based on preset performance control, a fuzzy neural network is used instead of the RBF neural network.

[0091] The beneficial effects of this invention are as follows:

[0092] (1) By considering the position output error constraint, a voice coil motor fast mirror control method based on preset performance control is proposed. The fast mirror tracking accuracy can be preset so as to ensure that the output behavior of the system such as overshoot and steady-state error is within the preset performance boundary, which effectively improves the transient and steady-state performance of the system.

[0093] (2) A neural network is introduced and the adaptive weight law of the neural network is derived. By continuously adjusting the weight of the neural network, the online fitting of unknown internal and external interference is achieved. The neural network model has a good fitting effect on unknown interference. A neural network fitting term is added to the input voltage control law of the fast mirror to achieve online compensation for unknown interference, which effectively improves the anti-interference ability of the system.

[0094] (3) An improved preset performance function is provided to replace the traditional preset performance function. The maximum convergence time is specified while retaining the preset tracking accuracy range. The system will converge to a stable state within the specified time.

[0095] (4) A fuzzy neural network is provided as an alternative to the RBF neural network, which achieves a better fitting ability for unknown internal and external interference quantities. Attached Figure Description

[0096] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the content of the embodiments of the present invention and these drawings without creative effort.

[0097] Figure 1 This is a schematic diagram of a voice coil motor fast-reflecting mirror control method based on preset performance control provided in an embodiment of the present invention;

[0098] Figure 2 This is a schematic diagram of the position tracking error of the voice coil motor fast-reflecting mirror provided in an embodiment of the present invention;

[0099] Figure 3 This is a schematic diagram of unknown interference experienced by the system provided in an embodiment of the present invention;

[0100] Figure 4 yes Figure 3 The fitting effect of the RBF neural network is shown in the figure. Detailed Implementation

[0101] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0102] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0103] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0104] In the description of this embodiment, the terms "upper," "lower," "left," and "right," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used solely for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In the description of the present invention, unless otherwise stated, "a plurality of" means two or more. Furthermore, the terms "first" and "second" are merely used for descriptive distinction and have no special meaning.

[0105] This invention provides a fast-reflecting mirror control method for a voice coil motor based on preset performance control, such as... Figure 1 As shown, the specific steps include:

[0106] A mathematical model of a voice coil motor fast-reflecting mirror is established, and the mathematical model is corrected by incorporating unknown internal and external disturbances.

[0107] Based on the mathematical model of the voice coil motor fast mirror, the design of the preset performance controller is carried out by combining the time-varying tangent barrier Lyapunov function and neural network. The control law of the voice coil motor fast mirror and the adaptive law of neural network weights are derived. The neural network fits the unknown internal and external disturbances.

[0108] Stability analysis was performed on the derived fast-reflection mirror control law for the voice coil motor.

[0109] Preferably, the voice coil motor is the core driver of the fast-reflecting mirror, and the accurate establishment of its mathematical model plays an important role in the design of the preset performance controller. Establishing the mathematical model of the voice coil motor fast-reflecting mirror, and modifying it in conjunction with unknown internal and external disturbances, specifically includes:

[0110] Based on the electrical characteristics of the voice coil motor, the voltage balance equation for the fast-reflecting mirror can be obtained as follows:

[0111]

[0112] in, For armature resistance, For armature current, For armature inductance, The back electromotive force coefficient, For the voice coil motor rotation angle, Armature input voltage;

[0113] Based on the mechanical characteristics of the voice coil motor, the torque balance equation for the fast-reflecting mirror can be obtained as follows:

[0114]

[0115] in, The moment of inertia of the motor. This is the viscous damping coefficient of the motor. The elastic coefficient of the flexible hinge. The electromagnetic torque constant is The angular velocity of the motor. This refers to the angular acceleration of the motor.

[0116] Combining equations (1) and (2) and performing a Laplace transform, we obtain:

[0117]

[0118] Eliminate intermediate variables The mathematical model of the voice coil motor fast-reflecting mirror can be obtained as follows:

[0119]

[0120] armature inductance The error is very small and negligible, and within the bandwidth of the fast-reflecting mirror, the fitting error of the second-order and third-order mathematical models of the fast-reflecting mirror is similar. Therefore, equation (4) can be simplified to:

[0121]

[0122] In the formula, , , ;

[0123] Equation (5) can be written in the form of a differential equation:

[0124]

[0125] Considering the numerous interferences that fast-reflecting mirrors will experience in practical applications, both internal (parameter perturbations, friction, etc.) and external (wind disturbances, mechanical vibrations, etc.), the mathematical model of the voice coil motor fast-reflecting mirror can be modified as follows:

[0126]

[0127] In the formula, This refers to the amount of unknown internal and external disturbances experienced by the system.

[0128] Preferably, based on the mathematical model of the voice coil motor fast-reflecting mirror, combined with the time-varying tangent barrier Lyapunov function and neural network, a preset performance controller is designed, deriving the control law of the voice coil motor fast-reflecting mirror and the adaptive weight law of the neural network. The neural network fits the unknown internal and external disturbances, specifically including:

[0129] Equation (7) can be written in the form of the following state-space equation:

[0130]

[0131] in, , The error variable is defined as:

[0132]

[0133] in, To give the position of the fast-reflecting mirror, For virtual control;

[0134] To ensure that the position output error always remains within the preset performance boundaries, the following preset performance function is constructed:

[0135]

[0136] in, To preset the initial performance values, To preset the performance steady-state error boundary value, , This is a positive constant used to adjust the preset performance convergence speed;

[0137] Define the first Lyapunov function as:

[0138]

[0139] Differentiating equation (11) yields:

[0140]

[0141] Substituting equation (9) into equation (12), we get:

[0142]

[0143] Design a virtual control law based on equation (13). for:

[0144]

[0145] in For the normal values ​​to be designed in the controller, the virtual control law is... Substituting into equation (13), we get:

[0146]

[0147] In addition, a sliding surface is introduced:

[0148]

[0149] in Let the positive constant be the second Lyapunov function, and define it as follows:

[0150]

[0151] in A constant greater than 0 For the ideal weights of the neural network, For the estimated weights of the neural network, To estimate the weights of the neural network, we can differentiate equation (17) to obtain:

[0152]

[0153] Substituting equation (7) of the mathematical model for the voice coil motor with a fast-reflecting mirror into equation (18), we get:

[0154]

[0155] Due to interference The magnitude is unknown; here, an RBF neural network is used to handle the interference. For overall approximation, the following definition is given based on neural networks:

[0156]

[0157]

[0158] in Let be the radial basis function, and let be the neural network input. , For neural network approximation error, To approximate the upper bound of the error, Let be the coordinate vector of the center point of the radial basis functions. Given the width of the radial basis functions, rewriting equation (19) yields:

[0159]

[0160] The control law for the output voltage of the voice coil motor fast-reflecting mirror, based on equation (22), is as follows:

[0161]

[0162] in, For the positive constants to be designed for the controller, For robustness, For positive numbers, take... Substituting equation (23) into equation (22) yields:

[0163]

[0164] And because when hour, ;

[0165] when hour, Therefore, equation (24) becomes:

[0166]

[0167] in Based on the above formula, design the adaptive law for neural network weights. for:

[0168]

[0169] in, , Substituting equation (26) into equation (25) for the controller parameters, we get:

[0170] .

[0172] Preferably, the stability analysis of the derived voice coil motor fast-reflection mirror control law specifically includes:

[0173] It can be rewritten as:

[0174]

[0175] According to Young's inequality, we can obtain:

[0176]

[0177] therefore:

[0178]

[0179] The system uses Lyapunov functions. Substituting equation (31) into equation (27) yields:

[0180]

[0181] in, , The expressions are as follows:

[0182]

[0183]

[0184] By Lyapunov's stability theorem and As can be seen from the expression, choosing appropriate parameters makes , , , , Both are bounded, since a given position of a fast-reflecting mirror and its derivative are also bounded, by... , The expression shows that the system's state variables are also bounded. From the virtual control law (14), the voice coil motor fast mirror output voltage control law (23), and the neural network weight adaptive law (26), we can see that they are also bounded. Therefore, all closed-loop signals of the system are consistent and eventually bounded.

[0185] Furthermore, preset the performance steady-state error boundary value. A smaller setting results in higher system stability and accuracy, but it also increases the control voltage. Similarly, a larger control gain... , It can also improve the control effect of the system, but it will also lead to an increase in the controller output. Therefore, the size of the control parameters should be selected reasonably by comprehensively considering the constraints and system performance.

[0186] Specifically, to verify the effectiveness of the above control method, simulation analysis was conducted based on MATLAB / Simulink. The frequency domain transfer model of the voice coil motor fast mirror is as follows: ; Controller gain , , Robust item The RBF neural network has 7 nodes, and the network center is set to... The width of the radial basis function is Neural network weight adaptive law , Preset initial values ​​for performance functions Preset performance steady-state error boundary value Convergence speed parameters The fast-reflection mirror tracking position is set to a frequency of 1. Amplitude 1 sinusoidal signal The amplitude of the unknown disturbance received by the system is .

[0187] Figure 2 The graph shows the position tracking error curve of the voice coil motor fast-reflector. It can be seen that, compared to the traditional PID control method, the tracking error of the control method of this invention always remains within the preset constraint boundary. The controller designed here enables the system to have high steady-state accuracy while having small overshoot and fast convergence speed, effectively improving the transient and steady-state performance of the system.

[0188] More specifically, the amount of unknown internal and external disturbances experienced by the system. The actual image and the neural network fitted image, such as Figure 3 and Figure 4 As shown, the designed neural network has an excellent fitting effect on nonlinear interference, making the present invention suitable for photoelectric tracking applications with limited field of view.

[0189] Optionally, this embodiment also constructs an improved preset performance function to replace the preset performance function (10), and the expression of the improved preset performance function is:

[0190]

[0191] in, To preset the initial performance values, To preset the steady-state error boundary value, take , This represents the convergence speed. For the maximum convergence time, we can derive it from equation (35). , , It decreases strictly and monotonically over time. Compared to traditional preset performance functions, the biggest advantage of the improved preset performance function is that it specifies the maximum convergence time while preserving the preset tracking accuracy range. The system will then converge within a specified time. It converges to a steady state.

[0192] Alternatively, a fuzzy neural network can be used instead of an RBF neural network. A fuzzy neural network is a combination of a fuzzy logic system and a neural network. It is built according to a fuzzy system model, and each node and parameter in the network has a specific physical meaning. Structurally, it resembles a neural network, but functionally it is a fuzzy system. Here is a set of examples of network membership functions that can form the nonlinear activation function of the fuzzy neural network of this invention:

[0193]

[0194] The biggest difference between fuzzy neural networks and RBF neural networks lies in their nonlinear activation functions. Unlike the radial basis functions used in RBF, fuzzy neural networks use fuzzy rules to construct fuzzy systems and employ product inference engines, single-valued fuzzers, and center-average defuzzers to design fuzzy controllers. Their nonlinear activation functions consist of a set of network membership functions, making them more complex in form and providing better fitting capabilities.

[0195] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0196] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A voice coil motor fast mirror control method based on preset performance control, characterized in that, Specifically comprising the following steps: A voice coil motor fast mirror mathematical model is established, and the voice coil motor fast mirror mathematical model is corrected in combination with internal and external unknown disturbance variables; According to the voice coil motor fast mirror mathematical model, a preset performance controller is designed in combination with a time-varying tangent type barrier Lyapunov function and a neural network, a voice coil motor fast mirror control law and a neural network weight self-adaptive law are derived, and the neural network is used to fit the internal and external unknown disturbance variables; Stability analysis is performed on the derived voice coil motor fast mirror control law.

2. The voice coil motor fast mirror control method based on preset performance control according to claim 1, characterized in that, The voice coil motor fast mirror mathematical model is established, and the voice coil motor fast mirror mathematical model is corrected in combination with internal and external unknown disturbance variables, specifically comprising: The fast mirror voltage balance equation can be obtained from the electrical characteristics of the voice coil motor: ， wherein, is the armature resistance, is the armature current, is the armature inductance, is the back EMF coefficient, is the voice coil motor angle, is the armature input voltage; The fast mirror torque balance equation can be obtained from the mechanical characteristics of the voice coil motor: ， wherein, is the motor rotational inertia, is the motor viscous damping coefficient, is the flexible joint spring constant, is the electromagnetic torque constant, is the motor angular velocity, is the motor angular acceleration; Equations (1) and (2) are combined and Laplace transformed to obtain: ， Eliminate intermediate variables The mathematical model of the fast mirror of the voice coil motor is obtained. ， The armature inductance Very small negligible, and in the fast mirror bandwidth range, fast mirror second three order mathematical model fitting error similar, therefore formula (4) can be simplified to: ， In the formulae, ; Equation (5) is written in the form of a differential equation: ， Considering that the fast mirror will be disturbed internally and externally in actual application, the voice coil motor fast mirror mathematical model is corrected to obtain: ， In the formula, is the unknown internal and external disturbance to the system. 3.The voice coil motor fast mirror control method based on preset performance control according to claim 2, wherein, According to the voice coil motor fast mirror mathematical model, a preset performance controller is designed in combination with a time-varying tangent type barrier Lyapunov function and a neural network, a voice coil motor fast mirror control law and a neural network weight self-adaptive law are derived, and the neural network is used to fit the internal and external unknown disturbance variables, specifically comprising: Equation (7) is written in the form of the following state space equation: ， wherein , the error variable is defined as: ， wherein, is a given position for the fast mirror, is a virtual control; To ensure that the position output error is always within the preset performance boundary, the following preset performance function is constructed: ， wherein, is a preset performance initial value, is a preset performance steady error boundary value, , is a normal number used to adjust the preset performance convergence speed; The first Lyapunov function is defined as: ， The derivative of equation (11) is obtained as: ， Equation (9) is substituted into equation (12) to obtain: ， The virtual control law is designed according to formula (13) is: ， wherein is the normal number to be designed for the controller, and the virtual control law Substituting equation (13) into equation (12) gives ， In addition, a sliding mode surface is introduced: ， wherein is a positive constant, define a second Lyapunov function as ， wherein is a constant greater than 0, is an ideal weight of the neural network, is an estimated weight of the neural network, is a neural network weight estimation error, and differentiating equation (17) gives ， The voice coil motor fast mirror mathematical model equation (7) is substituted into equation (18) to obtain: ， The interference quantity The size is unknown, here the RBF neural network is used to approximate the interference quantity The following definition is given based on the neural network: ， ， where is the radial basis function, and the neural network input is , is the neural network approximation error, is the approximation error upper bound, is the radial basis function center point coordinate vector, is the radial basis function width, and rewriting equation (19) gives ， According to equation (22), the voice coil motor fast mirror output voltage control law is designed as: ， wherein is a normal number to be designed for the controller, is a robust term, is a normal number, taken as Substituting equation (23) into equation (22) gives ， Also because when time, ; When time, Thus, equation (24) becomes: ， wherein , the neural network weight adaptation law is designed according to the above formula is: ， wherein , Substituting equation (26) into equation (25) for the controller parameters gives: 。 4. The voice coil motor fast mirror control method based on preset performance control according to claim 3, characterized in that, Stability analysis is performed on the derived voice coil motor fast mirror control law, specifically comprising: may be rewritten as: ， According to Young's inequality, it can be obtained that: ， Therefore: ， The system selects the Lyapunov function And substituting equation (31) into equation (27) gives: ， wherein , the expressions of which are respectively: ， ， From Lyapunov stability theorem and the expressions, it can be seen that the appropriate parameters are selected to make all bounded, since the given fast mirror given position and its derivative are also bounded, from , the expression can be derived that the system state variables are also bounded, from the virtual control law formula (14), the voice coil motor fast mirror output voltage control law formula (23), the neural network weight adaptive law formula (26) can be derived that they are also bounded, so all closed loop signals of the system are uniformly ultimately bounded.

5. The voice coil motor fast mirror control method based on preset performance control according to claim 3, characterized in that, An improved preset performance function is constructed to replace the preset performance function equation (10), and the expression of the improved preset performance function is: ， wherein, is a preset performance initial value, is a preset performance steady error boundary value, taken , represents the convergence speed, is the maximum convergence time, which can be derived from equation (35) strictly monotonically decreasing over time.

6. The voice coil motor fast mirror control method based on preset performance control according to claim 3, characterized in that, A fuzzy neural network is used to replace the RBF neural network.

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