Multi-axis servo turntable system and control method

Abstract

The invention discloses a multi-axis servo turntable system and a control method, and belongs to the technical field of mechanical control. The multi-axis servo rotary table system comprises a driving mechanism, a control unit and a rotary table body, and the driving mechanism comprises a plurality of servo motors and is used for driving the rotary table body to rotate and axially move; and the control unit controls each servo motor by using a fuzzy adaptive sliding mode iterative learning control algorithm. The sliding mode parameters are dynamically adjusted by introducing fuzzy logic, and the compensation of periodic disturbance by iterative learning is combined, so that the system buffeting is remarkably reduced, the dynamic response speed is improved, and the high-precision control of multi-axis collaboration is realized.

Description

Technical Field

[0001] The present invention relates to the field of mechanical control technology, and more particularly to a multi-axis servo turntable system and a control method. Background Art

[0002] Currently, the demand for target monitoring in optical systems is increasing, particularly in fields such as space exploration, remote sensing, and military reconnaissance. This is typically achieved using a freely rotating multi-axis servo turntable. However, existing multi-axis servo turntables, which mostly rely on analog signal control, have drawbacks such as low monitoring accuracy and slow response speed, making it difficult to achieve high-precision position and speed control.

[0003] Therefore, how to improve the control accuracy of the multi-axis servo turntable system is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a multi-axis servo turntable system and a control method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] On the one hand, the present invention discloses a multi-axis servo turntable system, comprising: a drive mechanism, a control unit and a turntable body, wherein the drive mechanism includes multiple servo motors for driving the turntable body to rotate and move axially; the control unit uses a fuzzy adaptive sliding mode iterative learning control algorithm to control each servo motor.

[0007] Preferably, multiple servo motors drive the turntable body to rotate and move axially, specifically including the following steps:

[0008] The servo controller of each servo motor receives feedback information from a corresponding encoder and controls each axis according to a fuzzy adaptive sliding mode iterative learning control algorithm preset by the control unit. The encoder is set on each axis.

[0009] Preferably, each axis is controlled according to a fuzzy adaptive sliding mode iterative learning control algorithm preset by the control unit, specifically comprising the following steps:

[0010] Constructing an adaptive sliding mode iterative learning controller, and using the sliding mode control rate of the adaptive sliding mode iterative learning controller as a state variable of each servo motor control unit, wherein the state variable includes the rotational speed of the servo motor;

[0011] The fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller;

[0012] The stability analysis and error convergence analysis of the optimized adaptive iterative learning sliding mode controller model are performed using the Lyapunov method.

[0013] An adaptive iterative learning sliding mode controller model that meets the stability and error convergence conditions is applied to the control unit of each servo motor.

[0014] Preferably, in the step of constructing an adaptive sliding mode iterative learning controller, the adaptive sliding mode iterative learning controller is expressed by the following formula:

[0015]

[0016] Among them, u k (t) is the k-th output of the adaptive sliding mode iterative learning controller; b -1 represents the reciprocal of the constant b; c is the integral coefficient, e k (t) is the tracking error of the k-th output speed; represents the desired target speed; B(x k ,t) is the known friction torque function; represents the desired iterative control component for learning the unknown periodic function; v k (t) represents the system state variable at the kth iteration; Represents the k-th disturbance estimate of the system.

[0017] Preferably, the system disturbance estimate Based on the following adaptive law:

[0018] ;

[0019] Where γ is the learning gain of the adaptive control law, and γ > 0; s k (t) represents the integrated sliding surface at the kth iteration.

[0020] Preferably, the fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller, which specifically includes the following steps:

[0021] Define the integral sliding surface s at the kth iteration k (t) and its rate of change As input variables of fuzzy logic modules;

[0022] Map the input variables to corresponding fuzzy sets through membership functions;

[0023] The center of gravity method is used to convert the fuzzy set output into parameter adjustment values ​​Δβ1 and Δc, where Δβ1 is the switching gain adjustment value and Δc is the integral coefficient adjustment value.

[0024] The optimized switching gain is obtained according to the switching gain adjustment amount Δβ1:

[0025] β1(k)=β1(k-1)+Δβ1;

[0026] The optimized integral coefficient is obtained according to the integral coefficient adjustment Δc:

[0027] c(k)=c(k-1)+Δc;

[0028] The optimized integral coefficient is applied to the adaptive sliding mode iterative learning controller to obtain the optimized adaptive sliding mode iterative learning controller.

[0029] Preferably, performing stability analysis and error convergence analysis on the optimized adaptive iterative learning sliding mode controller model by the Lyapunov method specifically includes the following steps:

[0030] Establish the Lyapunov function at the kth iteration:

[0031]

[0032] Calculate multiple Lyapunov subfunctions respectively according to the Lyapunov function 、 、 、 :

[0033]

[0034]

[0035]

[0036]

[0037] Where η1 and η2 represent system parameter constants; s k (t) represents the integrated synovial surface at the kth iteration; represents the learning error of the kth iteration; q represents a constant; represents the disturbance estimation error of the kth iteration; γ represents the learning gain of the adaptive control law;

[0038] Compute the difference between the kth iteration and the k-1th iteration for each Lyapunov subfunction , i=1,2,3,4;

[0039] Will get , i=1,2,3,4 sum to get the total difference ΔV between the kth iteration and the k-1th iteration of the Lyapunov function k (t);

[0040] Let the total difference ΔV between the kth iteration and the k-1th iteration of the Lyapunov function be k (t) is less than or equal to 0, then the adaptive iterative learning sliding mode controller model meets the stability and error convergence conditions.

[0041] Preferably, the multi-axis servo turntable system further includes a monitoring unit for real-time monitoring of the status and position information of each axis and for implementing alarms in abnormal situations.

[0042] Preferably, the multi-axis servo turntable system is applied to target monitoring of various optical systems.

[0043] On the other hand, the present invention also discloses a multi-axis servo turntable system control method, comprising the following steps:

[0044] The following steps are involved:

[0045] The servo motor speed is selected as the system state variable, and the parameter perturbation and external disturbance are considered to establish the servo motor dynamic system;

[0046] Constructing an adaptive sliding mode iterative learning controller, and using the sliding film control rate of the adaptive sliding mode iterative learning controller as a state variable of each servo motor control unit;

[0047] The fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller;

[0048] The stability analysis and error convergence analysis of the optimized adaptive iterative learning sliding mode controller model are performed using the Lyapunov method.

[0049] An adaptive iterative learning sliding mode controller model that meets the stability and error convergence conditions is applied to the control unit of each servo motor.

[0050] Furthermore, in the step of constructing an adaptive sliding mode iterative learning controller, the adaptive sliding mode iterative learning controller is expressed by the following formula:

[0051]

[0052] Among them, u k (t) is the k-th output of the adaptive sliding mode iterative learning controller; b -1represents the reciprocal of the constant b; c is the integral coefficient, e k (t) is the tracking error of the k-th output speed; represents the desired target speed; B(x k ,t) is the known friction torque function; represents the desired iterative control component for learning the unknown periodic function; v k (t) represents the system state variable at the kth iteration; Represents the k-th disturbance estimate of the system.

[0053] Furthermore, the fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller, which specifically includes the following steps:

[0054] Define the integral sliding surface s at the kth iteration k (t) and its rate of change As input variables of fuzzy logic modules;

[0055] Map the input variables to corresponding fuzzy sets through membership functions;

[0056] The center of gravity method is used to convert the fuzzy set output into parameter adjustment values ​​Δβ1 and Δc, where Δβ1 is the switching gain adjustment value and Δc is the integral coefficient adjustment value.

[0057] The optimized switching gain is obtained according to the switching gain adjustment amount Δβ1:

[0058] β1(k)=β1(k-1)+Δβ1;

[0059] The optimized integral coefficient is obtained according to the integral coefficient adjustment Δc:

[0060] c(k)=c(k-1)+Δc;

[0061] The optimized integral coefficient is applied to the adaptive sliding mode iterative learning controller to obtain the optimized adaptive sliding mode iterative learning controller.

[0062] Furthermore, it also includes using a neural network to estimate the nonlinear disturbance state of the system in real time.

[0063] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides a multi-axis servo turntable system and control method, which have the following beneficial effects:

[0064] The present invention introduces fuzzy logic to dynamically adjust sliding mode parameters and combines iterative learning to compensate for periodic disturbances, thereby significantly reducing system chattering and improving dynamic response speed, thereby achieving high-precision control of multi-axis collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0066] Figure 1 This is a schematic structural diagram of the multi-axis servo turntable system provided by the present invention.

[0067] Figure 2 This is a schematic diagram of the overall flow of the multi-axis servo turntable system control method provided by the present invention.

[0068] Figure 3 : This is a fuzzy sliding mode control architecture diagram provided by the present invention, used to demonstrate the integrated relationship between the fuzzy reasoning module and the sliding mode controller;

[0069] Figure 4 :This is the neural network online identification flow chart provided by the present invention, which is used to describe the neural network input / output and weight update process DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0071] The embodiment of the present invention first discloses a multi-axis servo turntable system for an optical system, such as Figure 1 The system includes: a driving mechanism, a control unit and a turntable body, wherein the driving mechanism includes multiple servo motors for driving the turntable body to rotate and move axially; the control unit uses an adaptive sliding mode iterative learning control algorithm to control each servo motor.

[0072] The turntable body of the multi-axis servo turntable system of this invention utilizes high-precision bearings to ensure stability and accuracy. The drive mechanism includes a servo motor and a matching reducer to drive the turntable body. The control unit, driven by the servo motor, precisely controls the position and speed of the turntable body using an adaptive sliding mode iterative learning control algorithm.

[0073] The multi-axis servo turntable of the present invention adopts a high-precision encoder for position feedback, which can achieve nanometer-level positioning accuracy. Through the precise control of the control system, the turntable can be accurately stopped at any position.

[0074] The multi-axis servo turntable of the present invention adopts high-precision bearings and mechanical structures, as well as an advanced control system, which can effectively reduce jitter and error and improve the stability of the turntable.

[0075] The multi-axis servo turntable of the present invention can be controlled through a simple operation interface, without the need for professional technicians to operate and maintain it. At the same time, the control system can also monitor various parameters of the turntable in real time to facilitate timely discovery and resolution of problems.

[0076] Based on the above system, the embodiment of the present invention also discloses a multi-axis servo turntable system control method for an optical system. The overall method is as follows: Figure 2 As shown, the specific implementation steps of the multi-axis servo turntable system control method are described below:

[0077] The present invention adopts an adaptive sliding mode iterative learning control algorithm: In order to facilitate the analysis of the stability of the adaptive sliding mode iterative learning algorithm (SMC-ILC), the motor speed will be selected as the system state variable. At the same time, parameter perturbations and external disturbances will be considered to establish a permanent magnet synchronous motor dynamic system as a single-input single-output system:

[0078] (1)

[0079] As an example, the specific implementation of the adaptive sliding mode iterative learning control strategy is explained, where x(t)=ω is the state variable of the system input (the system input can be the motor speed), u(t) = i qref is the control variable input (the control variable can be the current reference value), y(t) = ω is the system output (the system output can be the motor speed), f(x, t) is a function to be learned about the state variable, b is a known non-zero constant, r(t) is the system parameter change and external disturbance, B(x, t) is a known friction torque function (which can represent the relationship between the system friction torque and the input speed).

[0080] In order to achieve the control goal, before designing the adaptive sliding mode iterative learning algorithm, let ω ref The speed loop control algorithm should ensure that the actual operating speed of the servo system can track the given expected speed ω ref , e(t) is the tracking error of the output speed, and e(t) =ω ref(t) -ω(t), represents the difference between the actual speed and the expected speed. In any time period [0,T], the initial state of the system output speed error satisfies:

[0081] The initial error is bounded: The initial error e(0) is finite, that is, the speed error at the initial moment of the system will not be too large.

[0082] The error change rate is bounded: The error change rate de / dt at the initial moment is also limited, that is, the speed error at the initial moment of the system will not change too quickly.

[0083] The integral of the error and its derivative is bounded: The integral of the error and its derivative at the initial moment is finite, that is, the accumulation of the speed error of the system over a period of time will not be too large.

[0084] The second-order integral of the error and its derivative is bounded: The second-order integral of the error and its derivative at the initial moment is finite, that is, the cumulative change of the speed error of the system over a period of time will not be too large.

[0085] Select the integral sliding surface s(t) as shown below:

[0086] (2)

[0087] Where c is the integral coefficient of the sliding surface (used to adjust the sliding surface reaching speed), and c > 0. Differencing both sides of the above equation with respect to time t:

[0088] (3)

[0089] Combining the above two formulas, we can get:

[0090] (4)

[0091] The above equations are the dynamic equations of the sliding surface. In general, the control input of the servo system is to make the system move towards the sliding surface. The progressive rate must satisfy the time derivative of the sliding surface σ> 0 to ensure the stability of the dynamic system. The role of the controller is to make the system state trajectory reach the sliding surface within a limited time. ; s(t) is the sliding surface, is the time derivative of the sliding surface.

[0092] The input control quantity u(t) must meet the sliding surface arrival condition. At this time, the state variable v of the servo system is t Expressed in terms of sliding film control rate, the selection of sliding mode control rate is as follows:

[0093] (5)

[0094] Where, the coefficient of the sign function, β1, > 0, is the switching gain. When s > 0, the sign function, sgn(s), = 1; when s = 0, the sign function, sgn(s), = 0; and when s < 0, sgn(s), = -1. β2, > 0, is the linear gain.

[0095] Substituting Equation (5) into Equation (4), the design of the kth iteration of the adaptive sliding mode iterative learning controller is obtained as follows: (6)

[0096] Where u k (t) is the k-th output of the adaptive sliding mode iterative learning controller; b -1 represents the reciprocal of the constant b; c is the integral coefficient, e k (t) is the tracking error of the k-th output speed; represents the desired target speed; B(x k ,t) is the known friction torque function; It represents the iterative control component (iterative control part) used to learn the unknown periodic function, which can be used to learn the unknown periodic function; v k (t) represents the system state variable at the kth iteration; k is the number of iterations of the controller, so for the function to be learned f(x k ,t) to learn and design the iterative learning control law as shown below:

[0097] (7)

[0098] In the formula, q, η1, η2 are all constants greater than zero, s k (t) is the sliding surface dynamics at the kth iteration. In order to improve the robustness of the iterative learning strategy, the concept of sliding mode control is combined in the design of this control law.

[0099] Combining equations (4) and (6), we can obtain the simplified form of the dynamic equation of the sliding surface:

[0100] (8)

[0101] The above formula shows that if Can accurately treat the learning function f(x k ,t) to learn, sliding mode control rate v k (t) can effectively suppress the disturbance r k (t), the sliding surface will approach zero.

[0102] According to the Lyapunov stability theorem, the adaptive sliding mode control method must satisfy the conditions for the existence of the sliding mode surface and be able to effectively suppress system disturbances. Therefore, a simple adaptive algorithm is designed here to achieve online estimation of the system disturbance r(t). The estimated value is used to compensate the control law. The design of the kth iteration of the adaptive sliding mode iterative learning controller is as follows:

[0103] (9)

[0104] Where, The disturbance estimation of the representative system is mainly based on the following adaptive law:

[0105] (10)

[0106] Here γ is the learning gain of the adaptive control law, and γ > 0.

[0107] The estimation error is defined as in, represents the disturbance estimate, r k (t) represents the true disturbance value.

[0108] Equations (5), (7), (9), and (10) define the adaptive sliding mode iterative learning control technology. Combining Equations (4) and (9), we can obtain:

[0109] (11)

[0110] The above formula shows that if Can accurately learn the function to be learned f(x k ,t), sliding mode control rate v k (t) can effectively control the disturbance error, and the sliding surface will approach zero.

[0111] In order to estimate the sliding surface dynamics and the convergence of the speed response error, and to analyze the stability of the control law designed above.

[0112] First, the Lyapunov function at the kth iteration is defined as:

[0113] (12)

[0114] The Lyapunov function defined in this application includes multiple Lyapunov subfunctions 、 、 、 , the specific calculation formula of each sub-function is as follows:

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] Where η1 and η2 represent system parameter constants; s k (t) represents the integrated synovial surface at the kth iteration; Represents the learning error of the kth iteration, which can be used Represent and obtain; q represents a constant; represents the disturbance estimation error of the kth iteration; γ represents the learning gain of the adaptive control law.

[0120] Secondly, analyze the difference between each Lyapunov subfunction in formula (12) at the kth iteration and the k-1th iteration. The specific steps are as follows:

[0121] Analysis function V k 1 (t), the difference between the kth iteration and the k-1th iteration is:

[0122] (13)

[0123] Combining equations (5) and (11), we can obtain:

[0124] (14)

[0125] Analysis function V k 2 (t), the difference between the kth iteration and the k-1th iteration is:

[0126] (15)

[0127] Combining equations (5) and (11), we can obtain:

[0128] (16)

[0129] Analysis function V k 3 (t), the difference between the kth iteration and the k-1th iteration is:

[0130] (17)

[0131] Since there is the following relationship in mathematics (ab) T (ab)- (ac) T (ac)= (cb)T (2(ab)- (bc)), the above formula can be simplified to the following form:

[0132] (18)

[0133] Combining formula (7), we can get:

[0134] (19)

[0135] Similarly, we can get:

[0136] (20)

[0137] Analysis function V k 4 (t), the difference between the kth iteration and the k-1th iteration is:

[0138] (twenty one)

[0139] Since the external disturbance is slower than the system state, combined with formula (10), we can get:

[0140] (twenty two)

[0141] Combining equations (14), (16), (20), and (21), we can obtain the difference between the Lyapunov function at the kth iteration and the k-1th iteration:

[0142] (twenty three)

[0143] Let the switching gain β1 satisfy the condition β1 ≥ |r|, then the above formula can be simplified to:

[0144] ΔV k (t)≤0(24)

[0145] According to the Lyapunov stability theorem, the Lyapunov function V defined above k (t) is convergent, and the sliding surface s k (t) approaches zero and satisfies the reachability condition, the servo system's speed loop tracking error can also asymptotically approach zero. The adaptive law added to the system can reduce the switching gain, ensuring robustness while also reducing the servo system's chatter.

[0146] Based on the above content, the present invention introduces a fuzzy logic module, which uses the fuzzy logic module to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and adaptive sliding mode iterative learning controller.

[0147] Figure 3 The integration relationship between the fuzzy logic module and the sliding mode controller is demonstrated; the fuzzy logic module includes:

[0148] Fuzzy input module, used to input variable sliding surface error s k (t) and its rate of change .

[0149] The fuzzification module maps the input variables into fuzzy sets (such as "negative large", "zero", "positive large") through triangular / trapezoidal membership functions.

[0150] Rule base, used to store representative rules (examples):

[0151] If s k For "negative big" and is “positive”, then Δβ1= +0.2.

[0152] The defuzzification module converts the fuzzy output into parameter adjustment values ​​Δβ1 and Δc using the center of gravity method.

[0153] The parameter dynamic adjustment module is used to update and adjust parameters. The parameter update formula is as follows:

[0154] β1(k)=β1(k-1)+Δβ1,c(k)=c(k-1)+Δc.

[0155] The arrow of the parameter dynamic adjustment module points to the original sliding mode controller, indicating real-time parameter feedback.

[0156] The fuzzy logic module is connected in series with the adaptive sliding mode iterative learning controller (SMC-ILC) to output the optimized control variable u k (t).

[0157] As another improved implementation, the present invention also introduces a neural network online identification system. Figure 4 This is a flowchart for online identification of neural networks, describing the neural network input / output and weight update process. The weight update path aims to ensure Lyapunov stability. The input layer inputs the system state variable x k (t). The hidden layer uses RBF (Radial Basis Function) neural network; basis function Take the Gaussian function as:

[0158] ;

[0159] The number of hidden layer nodes is 5;

[0160] The output layer outputs "compensation for nonlinear perturbations", and the specific output layer outputs the nonlinear function estimate , the formula is:

[0161] ;

[0162] The weight update is based on the sliding surface error s k (t), fed back to the weight matrix W k (t), the update law is:

[0163] ;

[0164] Figure 4 The middle arrow marks the direction of "online learning".

[0165] Finally, the output is controlled by compensation And input to the controller to offset the nonlinear terms of the system.

[0166] Multi-axis servo turntables are suitable for target monitoring in various optical systems. Monitoring is accomplished through sensors, such as optical sensors, current and voltage sensors, and the operating status can be determined based on the corresponding signal feedback. For example, current and voltage sensors can be used to detect the current in the servo motor and analyze whether the turntable is operating properly. Alternatively, using a camera for target monitoring can enable visual feedback control, allowing real-time monitoring of the turntable's position and posture, and adjusting and controlling its correct position. This method is very effective for posture recognition and position control of rotating objects. Furthermore, machine vision technology can be used to monitor the target position of optical systems in real time. Algorithms can be used to process image data, extract key information, and evaluate the turntable's motion status. Software can also be used to process and analyze the acquired data, enabling turntable motion control and optical system fault diagnosis.

[0167] Specifically, cameras are mounted on the rotating body of the turntable to capture multi-directional images of the target object. The optical system and servo motors are mechanically connected within the device. Image data is typically collected by cameras or other imaging devices mounted around the optical system. These devices capture the target's position and environmental changes in real time. The captured image data includes visual information such as the target's position, shape, and color, as well as the turntable's motion.

[0168] The captured image data is analyzed and processed using image processing algorithms. These algorithms include, but are not limited to, image preprocessing (such as denoising and enhancement), target detection, edge detection, and morphological processing. During processing, the algorithms extract key information such as the target's position and morphological features. This key information primarily includes the target's current position, motion trajectory, rotational speed, acceleration, and other data. This information reflects the target's motion state within the optical system. The extracted key information is then compared and analyzed with the turntable's motion parameters. This comparison allows for an assessment of whether the turntable's current motion state meets expectations. For example, if the target's actual position deviates from the expected position, the system can correct the deviation by adjusting the turntable's motion parameters.

[0169] The motion of the turntable directly affects the target position of the optical system. Image data provides real-time feedback on the target position, helping to adjust the turntable's motion for optimal operation. This closed-loop control ensures that the optical system is always optimally aligned, improving system efficiency and accuracy. In summary, image data processing and key information extraction are achieved through real-time monitoring and analysis of the target position. Furthermore, by adjusting the turntable's motion, the optical system can accurately track and locate the target. Therefore, this invention has broad application prospects.

[0170] The multi-axis servo turntable achieves high-precision target positioning and rotation through the coordinated operation of multiple axes, with tracking accuracy reaching 30urad. Each axis is equipped with a high-precision encoder, providing accurate and reliable position feedback. Furthermore, a monitoring system monitors the status and position of each axis in real time, enhancing system safety and stability.

[0171] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0172] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-axis servo turntable system, characterized in that: include: A drive mechanism, a control unit, and a turntable body, wherein the drive mechanism includes multiple servo motors for driving the turntable body to rotate and move axially; the control unit uses a fuzzy adaptive sliding mode iterative learning control algorithm to control each servo motor, specifically including: Constructing an adaptive sliding mode iterative learning controller, and using the sliding mode control rate of the adaptive sliding mode iterative learning controller as a state variable of each servo motor control unit, wherein the state variable includes the rotational speed of the servo motor; The fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller; The stability analysis and error convergence analysis of the optimized adaptive iterative learning sliding mode controller model are performed using the Lyapunov method. An adaptive iterative learning sliding mode controller model that meets the stability and error convergence conditions is applied to the control unit of each servo motor.

2. The multi-axis servo turntable system according to claim 1, characterized in that: Multiple servo motors drive the turntable body to rotate and move axially, specifically including the following steps: The servo controller of each servo motor receives feedback information from a corresponding encoder and controls each axis according to a fuzzy adaptive sliding mode iterative learning control algorithm preset by the control unit. The encoder is set on each axis.

3. The multi-axis servo turntable system according to claim 1, characterized in that: In the step of constructing an adaptive sliding mode iterative learning controller, the adaptive sliding mode iterative learning controller is expressed by the following formula: ； Among them, u k (t) is the k-th output of the adaptive sliding mode iterative learning controller; b -1 represents the reciprocal of the constant b; c is the integral coefficient, e k (t) is the tracking error of the k-th output speed; represents the desired target speed; B(x k ,t) is the known friction torque function; represents the desired iterative control component for learning the unknown periodic function; v k (t) represents the system state variable at the kth iteration; Represents the k-th disturbance estimate of the system.

4. The multi-axis servo turntable system according to claim 1, characterized in that: The fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller, which specifically includes the following steps: Define the integral synovial surface s at the kth iteration k (t) and its rate of change As input variables of fuzzy logic modules; Map the input variables to corresponding fuzzy sets through membership functions; The center of gravity method is used to convert the fuzzy set output into parameter adjustment values ​​Δβ1 and Δc, where Δβ1 is the switching gain adjustment value and Δc is the integral coefficient adjustment value. The optimized switching gain is obtained according to the switching gain adjustment amount Δβ1: β1(k)=β1(k-1)+Δβ1 The optimized integral coefficient is obtained according to the integral coefficient adjustment Δc: c(k)=c(k-1)+Δc The optimized integral coefficient is applied to the adaptive sliding mode iterative learning controller to obtain the optimized adaptive sliding mode iterative learning controller.

5. The multi-axis servo turntable system according to claim 1, characterized in that: The stability analysis and error convergence analysis of the optimized adaptive iterative learning sliding mode controller model using the Lyapunov method specifically include the following steps: Establish the Lyapunov function at the kth iteration: ； Calculate multiple Lyapunov subfunctions respectively according to the Lyapunov function 、 、 、 : ； ； ； ； Where η1 and η2 represent system parameter constants; s k (t) represents the integrated synovial surface at the kth iteration; represents the learning error of the kth iteration; q represents a constant; represents the disturbance estimation error of the kth iteration; γ represents the learning gain of the adaptive control law; Compute the difference between the kth iteration and the k-1th iteration for each Lyapunov subfunction , i=1,2,3,4; Will get , i=1,2,3,4 sum to get the total difference ΔV between the kth iteration and the k-1th iteration of the Lyapunov function k (t); Let the total difference ΔV between the kth iteration and the k-1th iteration of the Lyapunov function be k (t) is less than or equal to 0, then the adaptive iterative learning sliding mode controller model meets the stability and error convergence conditions.

6. A multi-axis servo turntable system control method, characterized in that: The following steps are involved: The servo motor speed is selected as the system state variable, and the parameter perturbation and external disturbance are considered to establish the servo motor dynamic system; Constructing an adaptive sliding mode iterative learning controller, and using the sliding film control rate of the adaptive sliding mode iterative learning controller as a state variable of each servo motor control unit; The fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller; The stability analysis and error convergence analysis of the optimized adaptive iterative learning sliding mode controller model are performed using the Lyapunov method. An adaptive iterative learning sliding mode controller model that meets the stability and error convergence conditions is applied to the control unit of each servo motor.

7. The multi-axis servo turntable system control method according to claim 6, characterized in that: In the step of constructing an adaptive sliding mode iterative learning controller, the adaptive sliding mode iterative learning controller is expressed by the following formula: ； Among them, u k (t) is the k-th output of the adaptive sliding mode iterative learning controller; b -1 represents the reciprocal of the constant b; c is the integral coefficient, e k (t) is the tracking error of the k-th output speed; represents the desired target speed; B(x k ,t) is the known friction torque function; represents the desired iterative control component for learning the unknown periodic function; v k (t) represents the system state variable at the kth iteration; Represents the k-th disturbance estimate of the system.

8. The multi-axis servo turntable system control method according to claim 6, characterized in that: The fuzzy logic module is used to dynamically adjust the switching gain of the sliding mode control rate and the integral coefficient of the adaptive sliding mode iterative learning controller to obtain the optimized switching gain and the adaptive sliding mode iterative learning controller, which specifically includes the following steps: Define the integral sliding surface s at the kth iteration k (t) and its rate of change As input variables of fuzzy logic modules; Map the input variables to corresponding fuzzy sets through membership functions; The center of gravity method is used to convert the fuzzy set output into parameter adjustment values ​​Δβ1 and Δc, where Δβ1 is the switching gain adjustment value and Δc is the integral coefficient adjustment value. The optimized switching gain is obtained according to the switching gain adjustment amount Δβ1: β1(k)=β1(k-1)+Δβ1 The optimized integral coefficient is obtained according to the integral coefficient adjustment Δc: c(k)=c(k-1)+Δc The optimized integral coefficient is applied to the adaptive sliding mode iterative learning controller to obtain the optimized adaptive sliding mode iterative learning controller.

9. The multi-axis servo turntable system control method according to claim 6, characterized in that: It also includes the use of neural networks to estimate the nonlinear disturbance state of the system in real time.

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