Servo system preset performance sliding mode control method based on spectrum normalization neural network

Abstract

The invention provides a servo system preset performance sliding mode control method based on a spectrum normalization neural network. According to the method, an undisturbed first servo system model and a second servo system model added with a wind interference model are established. Inputting the two models based on the same control quantity to generate system state data, calculating a wind moment Td by using the difference of the system state data, and obtaining a training sample; training a spectrum normalization neural network by using the training sample, so that the spectrum normalization neural network has the capability of estimating the wind moment Td; during actual control, according to system state data output by a turntable servo system, a spectrum normalization neural network is utilized to obtain wind moment estimation # imgabs0 #, the wind moment estimation # imgabs1 # is compensated to a preset performance sliding mode controller, and a control quantity u is generated. According to the invention, the robustness of the control scheme can be improved.

Description

Technical Field

[0001] The present invention relates to the field of actual servo system compensation control, and in particular to a spectral normalized neural network preset performance sliding mode control method for a servo system disturbed by gusts of wind. Background Art

[0002] With the rapid development of modern industry, key fields such as precision manufacturing, aerospace, and robotics have put forward more stringent requirements on the control accuracy of servo systems. The control accuracy of the servo system is not only directly related to the tracking performance of the system, but also has a profound impact on the stability and response speed of the entire system. However, in the actual operation process, the servo system is often restricted by multiple factors such as nonlinear friction and unknown external interference, which makes it difficult to further improve the control accuracy of the system, seriously hindering the further development of high-end manufacturing and automation technology.

[0003] Deep learning, especially neural network architecture, has shown great achievements and wide application potential in many fields such as image generation, semantic segmentation, and reinforcement learning. However, neural networks generally face stability challenges during the training phase, such as key problems such as gradient vanishing or gradient exploding, which hinder the convergence speed of the model and affect its final performance. In the specific field of generative adversarial networks (GANs), the Jensen-Shannon (JS) divergence used in the objective function optimization process often causes the generator gradient to vanish easily, which in turn increases the difficulty and uncertainty of training. In order to meet this challenge, spectral normalization technology came into being and became a key innovation to solve the stability problem of neural networks. This technology calculates the spectral norm (maximum singular value) of the weight matrix of each layer and divides it by its spectral norm to ensure that the output of the network layer does not change too drastically. In the application scenario of GANs, spectral normalization technology is mainly used to constrain the discriminator to satisfy the 1-Lipschitz condition. The satisfaction of this condition effectively alleviates the instability in the training process and significantly improves the training efficiency and model performance of GANs.

[0004] Sliding mode control is a nonlinear robust control method. The core is to design a sliding surface without model dependence. Feedback and robust compensation are used to ensure that the system state reaches the sliding surface first and then slides along it to the equilibrium point. In this process, the movement of the system state is independent of the model and disturbance, so it is highly robust to uncertain interference. In order to optimize performance, strategies such as integral sliding mode to reduce errors, terminal sliding mode to promote finite time convergence, and fast terminal sliding mode to speed up have been developed. However, in order to deal with unknown interference, the commonly used sign function terms cause the sliding mode variables to switch near the sliding surface, resulting in chattering problems.

[0005] The specified performance control strategy aims to convert the system tracking error into an unconstrained form through a performance function, and ensure that the converted error remains continuous and well-bounded through a control method to meet the strict requirements of the system tracking performance. However, this approach may face singularity challenges, that is, when the tracking error approaches zero, the calculation of the control quantity may increase abnormally, causing the system to exceed the preset performance boundary and possibly become unstable. In addition, in actual engineering applications, situations such as sudden switching of work tasks, rapid changes in external interference, or difficulties in accurately predicting the initial tracking error may cause the system tracking error to exceed the specified performance range. Summary of the invention

[0006] In view of this, in order to solve the control problem of the servo system under gust interference, the present invention provides a preset performance sliding mode compensation control method based on spectral normalization neural network, which improves the robustness of the control scheme.

[0007] In order to solve the above technical problems, the present invention is implemented as follows.

[0008] A servo system preset performance sliding mode control method based on spectral normalization neural network, comprising:

[0009] Step 1: For the turntable servo system, build a disturbance-free first servo system model;

[0010] Step 2: Construct a wind disturbance model and convert the wind moment T output by the wind disturbance model into d Add the first servo system model to obtain a second servo system model including gust interference;

[0011] Step 3: The first servo system model and the second servo system model generate system state data based on the same control variable input, and the wind torque T is calculated based on the difference between the system state data generated by the two servo system models. d ; The system state data generated by the second servo system model containing gust interference and the calculated wind torque T d , forming training samples;

[0012] Step 4: Use the training samples to train the spectral normalization neural network to have wind moment T d Estimation ability;

[0013] Step 5: Construct a preset performance sliding mode controller: The tracking error e of the turntable servo system is first converted into a conversion error ε according to the preset performance function, and then the conversion error ε is used for sliding mode control to output the control quantity u;

[0014] Step 6: During actual control, the wind moment estimation is obtained using the spectral normalization neural network according to the system status data output by the turntable servo system The wind moment is estimated Compensate into the preset performance sliding mode controller to generate the control variable u.

[0015] Preferably, the disturbance-free first servo system model is:

[0016]

[0017] The second servo system model containing gust interference is:

[0018]

[0019] in,

[0020] q is the angular position, is the angular velocity; J is the motor inertia; K1=K T / R,K2=K T K E / R,K E =n P ψ f ; R is the rotor resistance, K T is the torque time constant, T L is the system load moment, n p is the number of electrodes, ψ f is the rotor flux; y is the angular position of the turntable servo system.

[0021] Preferably, in the wind disturbance model, the wind speed v is a steady-state wind speed v m and pulsating wind speed v g sum;

[0022] The steady state wind speed Among them, v m is the steady-state wind speed at height h, v mh is the reference height h m The steady-state wind speed at , α is the ground roughness index;

[0023] The pulsating wind speed v g The method of obtaining is: construct a filter function H so that its frequency response curve is close to the Davenport spectrum curve; pass the white noise with a mean of zero through this filter function H, and the filter function H outputs the pulsating wind speed v g ;

[0024] Substitute the wind speed v model into the following wind moment T d Calculation formula to obtain the wind interference model:

[0025] T d =k t pAD

[0026] Among them, k tRepresents the wind moment coefficient, p is the wind pressure, p=1 / 2ρv 2 , ρ is the air density, A is the area of ​​the circular cross-section of the turntable servo system, and D is the diameter of the circular cross-section of the turntable servo system.

[0027] Preferably, the filter function H is:

[0028]

[0029] Where s is the Laplace operator.

[0030] Preferably, the input of the spectral normalization neural network is the system state data of the turntable servo system, including the angular position q and the angular velocity

[0031] Preferably, in step 3, the wind torque T is calculated based on the difference of the system state data generated by the two servo system models. d for;

[0032] Get the output angular velocity of the two servo system models The difference Calculate wind moment

[0033] Preferably, in step 5, the tracking error e of the turntable servo system is first converted into a conversion error ε according to a preset performance function:

[0034]

[0035] Where, λ(t)=e(t) / ρ(t), e(t) is the tracking error of the turntable servo system, ρ(t) is ρ(t)=(ρ0-ρ ∞ ) -at +ρ ∞ is the performance function, ρ0 is the initial value range of the tracking error specified by the performance function, ρ ∞ is the steady-state boundary of the tracking error when the time t specified by the performance function approaches positive infinity; a is the attenuation factor; the generated conversion error ε(t) is taken as the error in the sliding surface.

[0036] Preferably, the sliding surface in the preset performance sliding mode controller is a linear sliding surface s: Where c is an adjustable gain greater than zero.

[0037] Preferably, the preset performance sliding mode controller is:

[0038]

[0039] In the above formula, q is the angular position, is the angular velocity, x 1dis the given angle, J is the motor inertia, T L is the system load torque; η and k are the parameters of the sliding surface, both of which are constants greater than zero; sgn(·) is the sign function, s represents the sliding surface; K1=K T / R,K2=K T K E / R,K T is the torque time constant, R is the rotor resistance, K E =n P ψ f , n p is the number of electrodes, ψ f is the rotor flux.

[0040] Beneficial effects:

[0041] (1) The present invention designs a spectral normalization neural network to approximate wind disturbance for unknown gust disturbance models. The powerful learning ability of the neural network can be used to predict and compensate for the complex disturbance caused by gust disturbances, effectively suppress gust disturbances, and improve the robustness of the control scheme. The spectral normalization technology can ensure the stability of the neural network during the training process, thereby improving its prediction accuracy and generalization ability.

[0042] (2) The present invention constructs a wind disturbance model through numerical simulation and generates training data for training a spectral normalization neural network to predict wind disturbance.

[0043] (3) In order to ensure the tracking performance, a preset performance sliding mode compensation controller is designed, which combines the specified performance control strategy with the sliding mode controller, and limits the position error to within the constraint range of the performance function through a preset performance function, thereby ensuring the convergence of the tracking error, making the control scheme of the present invention more robust and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 The present invention is a flow chart of a preset performance sliding mode compensation control method based on a spectral normalized neural network.

[0045] Figure 2 It is a control block diagram of the preset performance sliding mode compensation control method based on spectral normalization neural network of the present invention.

[0046] Figure 3 Schematic diagram of the structure of the turntable servo system considered in the present invention.

[0047] Figure 4 Schematic diagram of wind force and wind torque.

[0048] Figure 5 The wind speed and wind moment of the wind disturbance model simulated according to the selected parameters.

[0049] Figure 6The wind moment is obtained by numerical simulation.

[0050] Figure 7 The present invention provides the position tracking and position error of the system under the preset performance sliding mode compensation control based on the spectral normalization neural network.

[0051] Figure 8 The invention provides speed tracking and speed error of the system under the preset performance sliding mode compensation control based on the spectral normalization neural network.

[0052] Fig. 9 These are the approximate value and true value of the wind disturbance of the neural network proposed in the method of the present invention.

[0053] Fig.10 Trajectory tracking and position error of the system under sliding mode control with preset performance without compensation.

[0054] Fig.11 Speed ​​tracking and speed error of the system under sliding mode control with preset performance without compensation.

[0055] Fig.12 Trajectory tracking and trajectory error of the system under sliding mode control without compensation.

[0056] Fig.13 It is the speed tracking and speed error of the system under the sliding mode control without compensation. DETAILED DESCRIPTION

[0057] The present invention provides a spectral normalized neural network preset performance sliding mode control method for a servo system disturbed by gusts. In order to realize external gust interference compensation, the method uses a spectral normalized neural network to estimate and compensate for the external gust interference in the servo system. The preset performance sliding mode controller can adjust the controller output according to the degree of proximity between the system state and the predefined constraints, thereby ensuring that the servo system can follow the reference trajectory within the specified state limit and ensuring the convergence of the tracking error.

[0058] In order to train the spectral normalization neural network, the present invention first performs dynamic modeling on the turntable servo system, constructs two servo system models without wind disturbance and with wind disturbance, inputs the same control quantity to the two models, and outputs the angular position respectively to calculate the angular velocity. Based on the difference in angular velocity between the two models, the wind torque sample data can be calculated, and the spectral normalization neural network is trained to have the ability to predict the wind torque so that it can be compensated in the controller during actual control.

[0059] The present invention is described in detail below with reference to the accompanying drawings and embodiments. Figure 1 The flowchart of the servo system preset performance sliding mode control method based on spectral normalized neural network of the present invention is shown. Figure 2The control block diagram of the present invention is shown. As shown in the figure, the method comprises the following steps:

[0060] Step 1: For the turntable servo system, build a disturbance-free first servo system model.

[0061] The present invention considers Figure 3 The turntable servo system shown in the figure. The system includes a permanent magnet synchronous motor, an encoder, a power supply system, a speed loop controller, and a position loop controller. The permanent magnet synchronous motor accurately controls the position and speed of the turntable through voltage. The encoder measures the position and speed and provides closed-loop feedback. The power supply system supplies power to the system. The speed loop controller adjusts the motor speed, receives encoder feedback and compares it with the set value, and adjusts the motor output to achieve speed control. The position loop controller ensures that the motor reaches the desired position, receives encoder position feedback, compares it with the set value, and adjusts the motor output to achieve precise position control.

[0062] The turntable servo system model considered by the present invention is as follows:

[0063]

[0064] Among them, i d is the d-axis current, u q is the current on the q axis, u d is the voltage of the d-axis, u q is the voltage of the q axis, R is the rotor resistance, L is the rotor inductance, n p is the number of electrodes, ψ f is the rotor flux, K T is the torque time constant, J is the motor inertia, T L is the system load moment, T f is the friction torque of the system, T d is the system disturbance torque, q is the angular position, is the angular velocity.

[0065] In the actual design process, the d-axis reference current It is usually set to zero, so formula (1) can be expressed as

[0066]

[0067] Where K E =n P ψ f .

[0068] In addition, in reality, the electromagnetic time constant L / R is very small compared to the mechanical time constant and can be approximated to zero. The state variable is defined as y is the output, then formula (2) can be expressed as

[0069]

[0070] Where K1 = K T / R,K2=K T K E / R.

[0071] The disturbance-free first servo system model established in this step does not include disturbance, so T d and T f Remove it, and the first servo system model constructed is:

[0072]

[0073] Step 2: Construct a wind disturbance model and convert the wind moment T output by the wind disturbance model into d The first servo system model is added to obtain the second servo system model including gust disturbance.

[0074] This step includes the following sub-steps:

[0075] Step 21: First, numerical simulation method is used to model wind disturbance v.

[0076] The main methods for determining the wind interference model are numerical simulation, field test and wind tunnel experiment. The present invention adopts the numerical simulation method to construct the wind interference model. Wind can be divided into steady wind and pulsating wind according to its periodic characteristics. Steady wind has a long period, and the direction and speed of action do not change with time; pulsating wind has a short period, and the direction and speed change randomly with time.

[0077] The wind speed v can be expressed as the sum of the steady-state wind speed and the pulsating wind speed, that is:

[0078] v=v m +v g (5)

[0079] where v m is the average steady-state wind speed, v g Pulsating wind speed.

[0080] ① Steady-state wind model v m

[0081] The steady-state wind speed decreases with decreasing height, and is usually determined by the average wind speed profile curve, which mainly includes logarithmic wind speed profile curve and exponential wind speed profile curve. The exponential wind speed profile model is widely used in engineering because of its simple calculation and high accuracy. Based on observation data, Davenport et al. obtained the relationship between exponential wind profiles in different sites, namely:

[0082]

[0083] where v m is the steady-state wind speed at height h, v mhis the reference height h m The steady-state wind speed at α is the ground roughness index, which is related to the topography. The reference values ​​of the ground roughness index for common terrains are shown in Table 1:

[0084] Table 1 Reference values ​​of surface roughness index for common terrains

[0085]

[0086] ② Pulsating wind model v g

[0087] Analysis of the measured samples of fluctuating wind speed shows that the fluctuating wind speed can be regarded as a Gaussian stationary random process with a mean of 0 (excluding the average wind speed). Its power spectrum conforms to the Davenport spectrum function, so this function can be used to process the Gaussian white noise to obtain the fluctuating wind speed v g . Davenport spectral functions v Affected by the average wind speed and ground roughness, it can be expressed as

[0088]

[0089]

[0090] where k s is the surface tension coefficient, w is the wind frequency, z is the height from the ground, and z0 is the ground roughness height.

[0091] When simulating wind speed, first construct a filter function H so that its frequency response curve is close to the Davenport spectrum curve; then, pass the white noise with a mean of zero through this filter function H to obtain the pulsating wind speed v g The filter function H is constructed as follows:

[0092]

[0093] Where s is the Laplace operator.

[0094] ③ Wind moment modeling

[0095] Schematic diagram of wind force and wind moment Figure 4 As shown, based on the above steady-state wind model and pulsating wind model, combined with formula (5), the wind speed v can be obtained, and then the wind moment T can be calculated according to the following formula: d :

[0096] T d =k t pAD (10)

[0097] where k t Represents the wind moment coefficient, p is the wind pressure, p=1 / 2ρv 2, ρ is the air density, A is the cross-sectional area of ​​the turntable servo system, which is regarded as a prototype, then A=πD 2 / 4, D is the diameter of the approximately circular cross-section of the turntable servo system.

[0098] Step 22: Add a wind interference model (Formula (10)) to Formula (3) to obtain the second servo system model with gust interference:

[0099]

[0100] Step 3: Generate training samples.

[0101] Since it is very difficult to directly collect the gust interference of the turntable through sensors, the inventor adopts an indirect method to calculate and obtain it as a training set.

[0102] In this step, the first servo system model and the second servo system model generate system state data based on the same control variable input, and the wind torque T is calculated based on the difference between the system state data generated by the two servo system models. d ; The system state data generated by the second servo system model containing gust interference and the calculated wind torque T d , forming training samples.

[0103] Build 2 sets of Figure 2 The control system shown in FIG. 1 adopts the first servo system model and the second servo system model respectively. When obtaining training samples, a PID control algorithm can be used to give a desired position x 1d The sine wave is used as the tracking signal, and the PID control algorithm generates the control quantity u, which is input to the first servo system model and the second servo system model at the same time. The two models each output the angular position q, and the angular velocity is calculated. In order to distinguish the two models, the output angular position of the first servo system model is recorded as q1, and the angular velocity is calculated as The output angular position of the second servo system model is recorded as q2, and the calculated angular velocity is The wind moment that can be calculated from the data generated by the two models is

[0104] The training samples are: q1, T d Among them, q1, is the neural network input, T d is the output of the neural network. The T generated in this step d Labels to use for the learning process.

[0105] Step 4: Use the training samples to train the spectral normalization neural network to have wind moment T d estimation ability.

[0106] The input of the spectral normalization neural network is q1, The output is T d During training, the input q1, From the first servo system model with wind disturbance; in actual control, q1, Collected from actual turntable servo system.

[0107] The spectral normalization algorithm stabilizes the training of the neural network by limiting the Lipschitz constant of the objective function, which helps to improve the generalization performance of the neural network.

[0108] For the nth layer of the neural network, input x n-1 With the output x n The relationship can be expressed as follows:

[0109] x n =a n (W n x n-1 +b n ) (11)

[0110] where a n (·) is the activation function of the neural network in this layer. Here, ReLU function is used. n is the network parameter matrix, b n is the bias of the network. For the convenience of derivation, n If the omission is performed, equation (12) can be written as

[0111] x n =D n W n x n-1 (12)

[0112] Where D n ReLU represents the function of the ReLU function, which is a diagonal matrix. When the input value is less than 0, the diagonal element is 0; when the input value is greater than 0, the diagonal element is 1. The ReLU neural network can be described as

[0113] f(x)=D N W N ···D1W1x (13)

[0114] The Lipschitz constraint places a requirement on the gradient of f(x)

[0115]

[0116] Where ||W i ||2 represents the spectral norm of the parameter matrix. For the diagonal matrix D n, the spectral norm is the maximum value of the diagonal elements, because the diagonal elements corresponding to ReLU are 0 or 1, so its spectral norm is less than or equal to 1. Therefore, the above formula can be expressed as

[0117]

[0118] In order to make f(x) satisfy the Lipschitz constraint, the above formula is spectrally normalized to obtain

[0119]

[0120] Simply put, by dividing the parameter matrix of each layer of the neural network by its spectral norm, the Lipschitz constant can be ensured to be less than or equal to 1. If you want to make this constant less than or equal to the set hyperparameter γ, you can i The following spectral normalization algorithm is used:

[0121]

[0122] Where γ is the Lipschitz constant expected by the neural network, N+1 is the number of neural network layers, and σ(·) is the spectral norm.

[0123] Step 5: Construct a preset performance sliding mode controller: The tracking error e of the turntable servo system is first converted into a conversion error ε according to the preset performance function, and then the conversion error ε is used for sliding mode control to output the control quantity u.

[0124] The preset performance control target is to ensure that the tracking error meets the preset transient performance and steady-state performance during the control process of the turntable servo system, that is,

[0125] -ρ(t)<e(t)<ρ(t) (18)where e(t)=y(t)-y d (t) is the tracking error, ρ(t) = (ρ0-ρ ∞ ) -at +ρ ∞ is the performance function. ρ0 is the initial value range of the tracking error specified by the performance function, ρ ∞ The steady-state boundary of the tracking error when the time t specified by the performance function approaches positive infinity; a is the attenuation factor.

[0126] According to the performance boundary ρ(t), the conversion error is designed to be

[0127]

[0128] Where λ(t)=e(t) / ρ(t).

[0129] Taking the derivative of the conversion error ε, we get

[0130]

[0131] Select Linear Sliding Surface

[0132]

[0133] Where c is an adjustable gain greater than zero, and the first-order derivative of the sliding surface is

[0134]

[0135] According to formula (22),

[0136]

[0137] in x 1d is the desired angular position.

[0138] Finally, the preset performance sliding mode controller based on spectral normalized neural network is designed as

[0139]

[0140] Where η and k are constants greater than zero, sgn(·) is the sign function, The spectral normalization neural network is used to analyze the gust disturbance T d The approximation result of .

[0141] Step 6: During actual control, the wind moment estimation is obtained using the spectral normalization neural network according to the system status data output by the turntable servo system The wind moment is estimated Compensate into the preset performance sliding mode controller to generate the control variable u.

[0142] The control system architecture remains the same Figure 2 As shown in the figure, the position of the turntable servo system is the actual turntable servo system. The angular position and angular velocity are collected from the actual turntable and input into the trained spectral normalization neural network to obtain the wind torque estimation. Formula (24) is used to generate the control variable u, which is output to the turntable servo system for continuous control.

[0143] This is the end of the process.

[0144] The technical solution disclosed in the present invention is simulated and verified as follows:

[0145] S1: In the mathematical model of the turntable servo system of the simulation object, J=0.1, K1=1, K2=0.2.

[0146] S2: The simulation parameters for wind load modeling are as follows: Steady-state wind speed v m =10m / s, axial wind force coefficient k A =1.2, the lateral wind moment coefficient is kc =0.4, wind moment coefficient k t =0.16, radius D = 0.5m, sampling time 0.1s, wind moment simulation results are as follows Figure 5 shown.

[0147] S3: The present invention uses the gust interference model to obtain data, and adopts a 5th-order autoregressive model to approximate wind interference. The data set is divided into 70% training, 15% verification, and 15% test. Then a 6-layer neural network (5-10-16-20-8-1 neurons) is constructed, and the system angle and angular velocity are input to predict the wind interference torque. Finally, the random gradient descent method is used to optimize the network parameters, and the verification set is used to prevent overfitting, and the test set is used to evaluate the effect. Finally, the trained neural network model is saved with the training results as shown in Figure 6 shown.

[0148] S4: The preset performance parameters are ρ(0)=2, ρ(∞)=0.05, α=6, the sliding mode parameter is c=15, and the reaching law parameter is η=5, k=8. Gust interference T d Normalize the neural network output using spectral normalization Approximation. The expected tracking signal is a sinusoidal signal y d = sin(πt). The initial value is designed to be x(0) = [1,0] T The simulation time is 10s. The simulation results are as follows Figure 7-9 shown.

[0149] Figure 7 and Figure 8 They are respectively the position tracking and position error and the speed tracking and speed error of the system under the preset performance sliding mode control based on the spectral normalized neural network. The results show that the error approaches zero rapidly after 0.5 seconds, and the control effect is good. Fig. 9 In the control process of the preset performance sliding mode controller, the spectral normalized neural network approximates the wind disturbance. It can be seen that the approximation value and the true value curve are approximately coincident, indicating that the neural network has a good approximation effect.

[0150] In order to verify the superiority of the proposed compensation algorithm based on spectral normalized neural network, this paper also carried out a preset performance sliding mode control simulation experiment without using spectral normalized neural network compensation as a comparison. Fig.10 and Fig.11 They are the position tracking and position error and the velocity tracking and velocity error of the system under the preset performance sliding mode control without compensation.

[0151] Fig.10 , Fig.11 and Figure 7 , Figure 8By comparison, it can be seen that the position error of the system under the preset performance sliding mode control with compensation and the position error of the system under the preset performance sliding mode control without compensation are both within the performance function constraint range, but the position error of the system is reduced compared with the case without compensation, thus verifying the effectiveness of the compensation algorithm. In addition, in order to verify the superiority of the preset performance sliding mode control algorithm, we conducted a simulation experiment to compare it with the traditional sliding mode control. Fig.12 and Fig.13 They are the position tracking and position error and the velocity tracking and velocity error of the system under sliding mode control. Fig.10 , Fig.11 By comparison, it can be seen that under the preset performance sliding mode control, the system's position tracking performance and speed tracking performance are better, and both the control accuracy and dynamic response speed are improved.

[0152] The above specific embodiments only describe the design principle of the present invention. The shapes and names of the components in the description may be different and are not limited. Therefore, those skilled in the art in the field of the present invention may modify or replace the technical solutions recorded in the above embodiments; and these modifications and replacements do not deviate from the creative purpose and technical solutions of the present invention and should all fall within the protection scope of the present invention.

Claims

1. A servo system preset performance sliding mode control method based on spectral normalization neural network, characterized in that: include: Step 1: For the turntable servo system, build a disturbance-free first servo system model; Step 2: Construct a wind disturbance model and convert the wind moment T output by the wind disturbance model into d Add the first servo system model to obtain a second servo system model including gust interference; Step 3: The first servo system model and the second servo system model generate system state data based on the same control variable input, and the wind torque T is calculated based on the difference between the system state data generated by the two servo system models. d ; The system state data generated by the second servo system model containing gust interference and the calculated wind torque T d , forming training samples; Step 4: Use the training samples to train the spectral normalization neural network to have wind moment T d Estimation ability; Step 5: Construct a preset performance sliding mode controller: The tracking error e of the turntable servo system is first converted into a conversion error ε according to the preset performance function, and then the conversion error ε is used for sliding mode control to output the control quantity u; Step 6: During actual control, the wind moment estimation is obtained using the spectral normalization neural network according to the system status data output by the turntable servo system Estimate the wind moment Compensate into the preset performance sliding mode controller to generate the control variable u.

2. The method according to claim 1, characterized in that The disturbance-free first servo system model is: The second servo system model containing gust interference is: in, q is the angular position, is the angular velocity; J is the motor inertia; K1=K T / R,K2=K T K E / R,K E =n P ψ f ; R is the rotor resistance, K T is the torque time constant, T L is the system load moment, n p is the number of electrodes, ψ f is the rotor flux; y is the angular position of the turntable servo system.

3. The method according to claim 1, characterized in that In the wind disturbance model, the wind speed v is the steady-state wind speed v m and pulsating wind speed v g sum; The steady state wind speed Among them, v m is the steady-state wind speed at height h, v mh is the reference height h m The steady-state wind speed at , α is the ground roughness index; The pulsating wind speed v g The method of obtaining is: construct a filter function H so that its frequency response curve is close to the Davenport spectrum curve; pass the white noise with a mean of zero through this filter function H, and the filter function H outputs the pulsating wind speed v g ; Substitute the wind speed v model into the following wind moment T d Calculation formula to obtain the wind interference model: T d =k t pAD Among them, k t Represents the wind moment coefficient, p is the wind pressure, p=1 / 2ρv 2 , ρ is the air density, A is the area of ​​the circular cross-section of the turntable servo system, and D is the diameter of the circular cross-section of the turntable servo system.

4. The method according to claim 3, characterized in that The filter function H is: Where s is the Laplace operator.

5. The method according to claim 1, characterized in that The input of the spectral normalization neural network is the system state data of the turntable servo system, including the angular position q and the angular velocity 6. The method according to claim 1, characterized in that In step 3, the wind torque T is calculated based on the difference of the system state data generated by the two servo system models. d for; Get the output angular velocity of the two servo system models The difference Calculate wind moment 7. The method according to claim 1, characterized in that In step 5, the tracking error e of the turntable servo system is first converted into a conversion error ε according to a preset performance function: Where, λ(t)=e(t) / ρ(t), e(t) is the tracking error of the turntable servo system, ρ(t) is ρ(t)=(ρ0-ρ ∞ ) -at +ρ ∞ is the performance function, ρ0 is the initial value range of the tracking error specified by the performance function, ρ ∞ is the steady-state boundary of the tracking error when the time t specified by the performance function approaches positive infinity; a is the attenuation factor; the generated conversion error ε(t) is taken as the error in the sliding surface.

8. The method according to claim 7, characterized in that The sliding surface in the preset performance sliding mode controller selects a linear sliding surface s: Where c is an adjustable gain greater than zero.

9. The method according to claim 8, characterized in that The preset performance sliding mode controller is: In the above formula, q is the angular position, is the angular velocity, x 1d is the given angle, J is the motor inertia, T L is the system load torque; η and k are the parameters of the sliding surface, both of which are constants greater than zero; sgn(·) is the sign function, s represents the sliding surface; K1=K T / R,K2=K T K E / R,K T is the torque time constant, R is the rotor resistance, K E =n P ψ f , n p is the number of electrodes, ψ f is the rotor flux.

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