Improved ADRC two-axis turntable control method

Abstract

The invention provides a two-axis turntable control method for improving ADRC, and solves the problems that in the prior art, in a complex environment, a two-axis servo turntable is easily interfered by uncertain factors such as friction interference, mass imbalance and a time delay phenomenon, and the stable tracking performance of the turntable is interfered; the invention provides a two-axis turntable control method for improving ADRC, and the method comprises the steps: obtaining a state signal of a target axis of a turntable in real time through building a two-axis servo turntable model; establishing an active disturbance rejection controller based on the two-axis servo turntable model, and generating a control signal of the target axis; comprising the steps of optimizing a tracking differentiator based on a particle swarm optimization algorithm, adjusting parameters of an extended state observer by using a radial basis function neural network, establishing feedback connection based on an active disturbance rejection controller model, feeding back and outputting a control signal of a target axis to a two-axis servo turntable model, and improving the stable tracking performance of the two-axis turntable.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a two-axis turntable control method for improving ADRC. Background Art

[0002] The control system is the core of the two-axis servo turntable, and it plays a vital role in the key performance indicator of tracking accuracy. The realization of high-precision tracking fundamentally depends on whether the system can be stably controlled. Since the carriers of the two-axis servo platform are usually aircraft, ships, tanks, etc., the working environment is harsh. In addition, due to the complexity of its own structure, the system usually has coupling and strong nonlinear problems, and even time lag. At the same time, the system is also affected by friction interference, mass imbalance torque interference, and external environmental uncertainty (such as carrier movement, external wind resistance torque, etc.). The existence of these problems will affect the stable tracking performance of the two-axis servo turntable. The traditional control method cannot enable the system to achieve the expected performance indicators when working in a complex environment, and even causes the task to fail to be completed smoothly. The design of the ADRC controller can effectively improve the anti-interference ability and stability of the turntable system through dynamic disturbance observation and compensation. However, ADRC still has some shortcomings in dealing with complex nonlinear and time-lag phenomena, resulting in unsatisfactory control performance in some complex environments. Summary of the invention

[0003] One of the embodiments of this specification provides a two-axis turntable control method for improving ADRC. The method includes:

[0004] Step S100: Establish a two-axis servo turntable model and obtain the state signal of the turntable target axis in real time;

[0005] Step S200: establishing an auto-disturbance rejection controller based on the two-axis servo turntable model to generate a control signal for the target axis;

[0006] Step S300: establishing a feedback connection based on the ADRC model, and outputting the control signal of the target axis as feedback to the two-axis servo turntable model;

[0007] The active disturbance rejection controller includes a tracking differentiator, an extended state observer and a nonlinear state error feedback model. The step S200 specifically includes:

[0008] Step S210: establishing a tracking differentiator based on a particle swarm optimization algorithm to obtain a desired state signal of the target axis;

[0009] Step S220: establishing an extended state observer based on a radial basis function neural network, and determining a system disturbance and an estimated state signal of the target axis under a disturbance state according to the state signal and the expected state signal of the target axis;

[0010] Step S230: Establish a nonlinear state error feedback model, obtain an error amount according to the expected state signal and the estimated state signal, perform nonlinear combination to form a series controller, and generate a control signal for the target axis.

[0011] The beneficial effects of the present invention are:

[0012] 1. Enhance the system's anti-interference ability: Through dynamic disturbance observation and compensation, it effectively solves the performance deficiencies of traditional control methods in complex environments and nonlinear problems, and improves the system's anti-interference ability.

[0013] 2. Improve control accuracy and robustness: RBF neural network enhances ESO's ability to estimate nonlinear disturbances, enabling ADRC to more accurately respond to external disturbances and the nonlinear problems of the system itself, thereby further improving the accuracy and robustness of the control system.

[0014] 3. Optimize system response speed: The PSO algorithm estimates the speed factor r and filter factor h in TD, making the system respond faster to disturbances and ensuring that ideal performance indicators can be achieved in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0016] Figure 1 is an exemplary flow chart of a two-axis turntable control method for improving ADRC according to some embodiments of this specification;

[0017] Figure 2 is an overall control block diagram of a two-axis turntable control system according to some embodiments of this specification;

[0018] Figure 3 It is a structural block diagram of an improved active disturbance rejection controller based on radial basis neural network and particle swarm optimization algorithm according to some embodiments of this specification;

[0019] Figure 4 It is a test effect diagram of the step response experiment of the two-axis turntable control system according to the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification;

[0020] Figure 5 It is a test effect diagram of a local amplification experiment of a two-axis turntable control system step response according to the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification;

[0021] Figure 6 It is an experimental test effect diagram of inputting a sinusoidal signal and adding an external disturbance to a two-axis turntable control system according to the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification;

[0022] Figure 7 It is a local amplification experimental test effect diagram of a two-axis turntable control system inputting a sinusoidal signal and adding an external disturbance according to the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification. DETAILED DESCRIPTION

[0023] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0024] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0025] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0026] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0027] Figure 1 is an exemplary flow chart of a two-axis turntable control method for improving ADRC according to some embodiments of the present specification.

[0028] Step S100: establishing a two-axis servo turntable model for obtaining the state signal of the turntable target axis in real time;

[0029] Step S200: establishing an auto-disturbance rejection controller based on the two-axis servo turntable model to generate a control signal for the target axis;

[0030] Step S300: establishing a feedback connection based on the ADRC model, and outputting the control signal of the target axis as feedback to the two-axis servo turntable model;

[0031] The active disturbance rejection controller includes a tracking differentiator, an extended state observer and a nonlinear state error feedback model. The step S200 specifically includes:

[0032] Step S210: establishing a tracking differentiator based on a particle swarm optimization algorithm to obtain a desired state signal of the target axis;

[0033] Step S220: establishing an extended state observer based on a radial basis function neural network, and determining a system disturbance and an estimated state signal of the target axis under a disturbance state according to the state signal and the expected state signal of the target axis;

[0034] Step S230: Establish a nonlinear state error feedback model, obtain an error amount according to the expected state signal and the estimated state signal, perform nonlinear combination to form a series controller, and generate a control signal for the target axis.

[0035] In some embodiments, the two-axis servo turntable is driven by a synchronous permanent magnet motor, and the torque balance model of the two-axis servo turntable is established as:

[0036]

[0037] Where J is the moment of inertia of the target axis, ω is the angular velocity of the target axis, and T e is the motor electromagnetic torque of the target axis, T L is the load torque of the target axis, F f is the friction torque of the target axis. In some embodiments, the moment of inertia can be calculated by simulating the physical characteristics of the turntable using simulation software, and used to establish a dynamic model of the two-axis servo turntable.

[0038] According to the Euler dynamics equation, the dynamic model of the target axis is established:

[0039]

[0040] J is the moment of inertia of the target axis, M x 、M y 、M z is the driving torque of the target axis on the X-axis, Y-axis and Z-axis, F f is the friction torque of the target axis, By establishing a dynamic model, the motion behavior of the two-axis turntable under the influence of various torques can be accurately described. This is mainly reflected in the change of angular velocity: It indicates the rate of change of the angular velocity of the axis, reflecting the rotation dynamics of the turntable on each axis.

[0041] like Figure 2 , which is an overall control block diagram of a two-axis servo turntable system described in some embodiments of this specification. In some embodiments, the two-axis servo turntable includes an inner and outer frame, wherein the inner frame is a pitch frame and the outer frame is an azimuth frame. The corresponding torque balance equation can be established according to the torque relationship of the inner frame or the outer frame of the turntable:

[0042]

[0043] In the formula, J out and J in are the moments of inertia of the outer frame and the inner frame, w out and w in are the angular velocities of the outer frame and the inner frame respectively, T eo and T ei are the electromagnetic torques of the motor for the outer frame and inner frame respectively, T Lo and T Li are the load torques of the outer frame and inner frame respectively, F fout and F fin are the friction moments of the outer frame and the inner frame respectively.

[0044] Furthermore, considering the coupling relationship between the inner and outer frames, according to the Euler dynamics equation, the dynamic models of the outer and inner frames are as follows:

[0045]

[0046]

[0047] In the formula, is the moment of inertia of the outer frame, is the moment of inertia of the inner frame; M xo 、M yo 、M zo M is the driving torque on the X-axis, Y-axis and Z-axis of the turntable frame; xi 、M yi 、M zi is the driving torque on the X-axis, Y-axis and Z-axis of the inner frame; is the outer frame friction torque, is the inner frame friction torque.

[0048] Figure 3It is a structural block diagram of the improved auto-disturbance rejection controller based on radial basis neural network and particle swarm optimization algorithm described in this specification. In some embodiments, an auto-disturbance rejection controller is established based on the two-axis servo turntable model, and the specific step S210 includes:

[0049] Construct a second-order nonlinear tracking differentiator. The expression of the second-order nonlinear tracking differentiator is as follows:

[0050]

[0051] Wherein, k and k+1 represent the two adjacent sampling moments respectively; state variables x1 and x2 represent the expected angle and expected angular velocity respectively; r is the velocity factor; h is the filter factor; T is the differential interval, and fhan(x1,x2,r,h) is the most rapid control comprehensive function;

[0052] make:

[0053]

[0054] Among them, d, d0, a, a0 are intermediate parameters; u(k) is the input expected state signal; sign is the sign function;

[0055] Based on the particle swarm optimization algorithm, the speed factor and filter in the second-order nonlinear tracking differentiator equation are optimized, and the expression is as follows:

[0056]

[0057] Wherein, PSOParams is the parameter array obtained by particle swarm optimization algorithm, PSOParams[0] and PSOParams[0] are used to adjust the speed factor r and the filter factor h respectively; a is the adjusted speed factor, and b is the adjusted filter factor.

[0058] In some embodiments, a conventional tracking differentiator (TD) needs to find a balance between fast response and smoothness of the output signal. Fast response may cause the signal to be too sharp, while excessive smoothness may cause a slow response. Therefore, the global search capability and fast convergence characteristics of the particle swarm optimization algorithm (PSO) are used to optimize the speed factor r and filter factor h in the TD. Through the iterative optimization process of PSO, the optimal combination of speed factor and filter factor is searched, so that the TD performs more stably when processing noise, reduces the overshoot phenomenon of the system, and ensures the smoothness and response speed of the output signal.

[0059] In some embodiments, the speed factor and the filter in the basic second-order nonlinear tracking differentiator equation are optimized based on the particle swarm optimization algorithm, including:

[0060] Initialize the particle swarm, each particle represents a combination of a speed factor r and a filter factor h.

[0061] Set the fitness function to evaluate the adjusted speed factor and filter factor.

[0062] The particle speed and position are updated according to the particle position, speed, individual optimal position and global optimal position.

[0063] Specifically, the particle speed and position update formula is:

[0064] v i (t+1)=wv i (t)+c1r1(p i,best -x i (t))+c2r2(g best -x i (t)) (9),

[0065] x i (t+1)=x i (t)+v i (t+1) (10),

[0066] In the formula, v i (t) is the velocity of particle i at time t; x i (t) is the position of particle i at time t; w is the inertia weight, which is used to control the influence of the current velocity of the particle; c1 and c2 are acceleration coefficients, which represent the acceleration of the particle moving to its own optimal position and the global optimal position; r1 and r2 are random numbers between [0,1] to increase randomness and diversity; p i,best is the best historical position of particle i; g best is the global best position found among all particles.

[0067] Repeat the fitness evaluation and speed and position update, and continuously update and record the current optimal particle, that is, the optimal combination of speed factor r and filter factor h.

[0068] When the preset stop condition is reached, the currently optimal combination of speed factor and filter factor is selected as the final result.

[0069] In some embodiments, step S220 specifically includes:

[0070] The state space equation expression corresponding to the two-axis servo turntable model is:

[0071]

[0072] Where x1 and x2 are the state signals of the target axis, and are the state variable angles θ of the target axis respectively. outand the state variable angular velocity Contains unknown disturbances and known disturbances, is the process variable of the state signal, the disturbance x3 is observed as a state, and the state space equation is expanded as follows:

[0073]

[0074] According to the state space equation, an extended state observer is established, and the equation is:

[0075]

[0076] Among them, y is the system output; z1 and z2 are the estimated state signals of the target axis; e1 is θ out The estimated error; z3 is the estimated value of the total disturbance f(x1,x2,ω(t),t); i is the control quantity output by the ADRC; β 01 , β 02 , β 03 Output correction factor for disturbance observation error; is the process variable for estimating the state signal.

[0077] The radial basis function neural network model is used to calculate the parameter β in the extended state observer. 01 , β 02 , β 03 Optimization is performed to optimize and correct the extended state observer.

[0078] In some embodiments, the radial basis neural network model is used to adjust the parameter β in the extended state observer. 01 , β 02 , β 03 The optimization also includes designing a radial basis function neural network model (RBF) and optimizing the parameters of the extended state observer based on the radial basis function neural network model.

[0079] Specifically, the radial basis neural network model is used to approximate complex nonlinear functions or control strategies, including the network's output weights, node basis width parameters, and node center values, and the iterative algorithm design.

[0080] Specifically, the RBF neural network model includes three layers: input layer, hidden layer and output layer.

[0081] Among them, the hidden layer uses Gaussian function as the basis function of the neuron, and the expression is as follows:

[0082]

[0083] In turntable control, the input vector of the identification neural network is:

[0084] X=[i q (k),θ(k),θ(k-1)] (15),

[0085] In the formula, θ(k) and i q (k) are the angle and current signals respectively.

[0086] Select the performance indicator function to describe the size of the approximation error:

[0087]

[0088] Where E(k) is the error performance index at the kth moment, θ(k) is the estimated angle at the kth moment, and θ r (k) is the target angle at the kth moment.

[0089] Since the error between the network output and the target output needs to be reduced, the network's output weights, node base width parameters, and node center values ​​must be derived.

[0090] The network output is directly affected by adjusting the weights in order to reduce the deviation between the output and the target. The network output weight is:

[0091] w j (k) = w j (k-1)+η(yy m )h j +α(w j (k-1)-w j (k-2)) (17),

[0092] Where: w j (k) is the value of the jth weight at the kth moment, η is the learning rate, h j is the radial basis function, α is the momentum factor, y m is the output of the neural network, and y is the actual rotation angle of the motor.

[0093] Adjusting the basis width helps change the "sensing" range of the radial basis function, thereby affecting the network's response sensitivity to different input data. Appropriate adjustment of the basis width can enhance the model's generalization ability or adaptability to specific data features. The node basis width parameter is:

[0094]

[0095] Where: Δb j (k) is the weight change at the kth moment, b j (k) is the updated weight, η is the learning rate, y m is the output of the neural network, y is the actual turning angle of the turntable, and w j is the network output weight, hj is the radial basis function, α is the momentum factor, X=[x1,x2,…,x n ] is the input vector, C j =[c j1 ,c j2 ,…c jn ] T is the center vector of the jth node in the network.

[0096] Adjusting the center position helps the radial basis function better align with the key features in the data, thereby improving the network's adaptability to the data structure and learning efficiency. The node center value is:

[0097]

[0098] Where: Δc j (k) is the center value of the jth node at the kth moment.

[0099] Furthermore, the powerful nonlinear mapping ability of RBF neural network is used to optimize the parameters of ADRC extended state observer online, approximating the nonlinear part of ESO and improving the accuracy of ESO's disturbance estimation, thereby enhancing the anti-interference ability of ADRC controller.

[0100] Specifically include:

[0101] About Beta 01 , β 02 , β 03 The adjustment adopts the gradient descent method, and the control error is defined as: e rr (k) = θ(k) - θ m (k); The index function of RBF for ESO controller tuning is:

[0102]

[0103] Use gradient descent to adjust β 01 , β 02 , β 03 Change:

[0104]

[0105] In the formula, ee=[de rr ,e rr ,dde rr ] is the error vector, where:

[0106] ee(1) is the first-order difference of the error, i.e., the angular velocity error; ee(2) is the angle error; and ee(3) is the second-order difference of the error, i.e., the angular acceleration error.

[0107] The expression of the improved extended state observer ESO is:

[0108]

[0109] In some embodiments, step 230 further includes: establishing a nonlinear state error feedback model, obtaining an error amount according to the desired state signal and the estimated state signal, performing nonlinear combination to form a series controller, and generating a control signal for the target axis.

[0110] Specifically, in the nonlinear state error feedback model (NLSEF), the system converts the state estimation output z of the extended state observer into i (i=1,2,…n) and the output v of the tracking differentiator TD i (i=1,2,…n) to get the error:

[0111] ε i =v i -z i (i=1,2,…n) (23),

[0112] Subtract the state signal of the tracking differentiator TD from the desired state signal in the extended state observer, and then perform nonlinear combination to convert it into a series controller form, and the corresponding NLSEF form can be obtained as follows:

[0113]

[0114] Where: i0 is the preset current control quantity; i is the final control quantity after adding the feedforward disturbance.

[0115] Simulation experiment

[0116] In some embodiments, the real-time monitoring and feedback connection between the improved ADRC and the two-axis servo turntable model is implemented and simulated and tested. Figures 4 to 7 As shown, Figure 4 It is a test effect diagram of the step response experiment of the two-axis turntable control system according to the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification; Figure 5 It is a test effect diagram of a local amplification experiment of a two-axis turntable control system step response according to the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification; Figure 6 The two-axis turntable control system is based on the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification. When the sinusoidal input signal θ(t) = -2sin(4πt) is added with external disturbance Experimental test effect diagram; Figure 7The two-axis turntable control system is based on the improved ADRC, traditional ADRC and three-closed-loop PID shown in some embodiments of this specification. When the sinusoidal input signal θ(t) = -2sin(4πt) is added with external disturbance The local magnification test effect diagram. The experimental results show that:

[0117] (1) Tracking accuracy is improved by 15% to 20% when responding to rapidly changing targets;

[0118] (2) Response time is shortened by 13% to 18%, allowing the system to adjust to the desired state more quickly;

[0119] (3) The suppression effect on external disturbances is improved by more than 21%, and the steady-state error of the system when disturbed is reduced.

[0120] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0121] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0122] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0123] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0124] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0125] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0126] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A two-axis turntable control method for improving ADRC, characterized in that: include: Step S100: Establish a two-axis servo turntable model and obtain the state signal of the turntable target axis in real time; Step S200: establishing an auto-disturbance rejection controller based on the two-axis servo turntable model to generate a control signal for the target axis; Step S300: establishing a feedback connection based on the ADRC model, and outputting the control signal of the target axis as feedback to the two-axis servo turntable model; The active disturbance rejection controller includes a tracking differentiator, an extended state observer and a nonlinear state error feedback model. The step S200 specifically includes: Step S210: establishing a tracking differentiator based on a particle swarm optimization algorithm to obtain a desired state signal of the target axis; Step S220: establishing an extended state observer based on a radial basis function neural network, and determining a system disturbance and an estimated state signal of the target axis under a disturbance state according to the state signal and the expected state signal of the target axis; Step S230: Establish a nonlinear state error feedback model, obtain an error amount according to the expected state signal and the estimated state signal, perform nonlinear combination to form a series controller, and generate a control signal for the target axis.

2. The improved ADRC two-axis turntable control method according to claim 1 is characterized in that: The step S210 includes: The speed factor and filter factor in the tracking differentiator equation are optimized based on the particle swarm optimization algorithm.

3. The improved ADRC two-axis turntable control method according to claim 2 is characterized in that: The particle swarm optimization algorithm is used to optimize the speed factor and filter in the tracking differentiator equation, including: Initialize the particle swarm, where each particle represents a combination of a speed factor and a filter factor; Setting a fitness function to evaluate the adjusted speed factor and filter factor; Update the particle speed and position according to the particle position, speed, individual optimal position and global optimal position; Repeat fitness evaluation and speed and position update to continuously update and record the current optimal particle; When the preset stop condition is reached, the currently optimal combination of speed factor and filter factor is selected as the final result.

4. The improved ADRC two-axis turntable control method according to claim 1 is characterized in that: Step S220 includes: Establishing a corresponding state space equation according to the two-axis servo turntable model; Add the disturbance state to the state space equation and establish an extended state observer; The radial basis neural network model is used to optimize the parameters in the extended state observer, and the extended state observer is optimized and corrected.

Images