Control method and system based on sliding mode controller and PD controller, and medium

Abstract

The invention discloses a control method and system based on a sliding mode controller and a PD controller and a medium, and the method comprises the steps: obtaining a target deviation value, and obtaining a gain coefficient and a bias coefficient of a target system based on the target deviation value; based on the gain coefficient and the bias coefficient, obtaining an initial parameter of a self-adaptive PD controller and an initial sliding surface of a self-adaptive sliding mode controller; and based on the initial parameter and the initial sliding surface, obtaining a control parameter, and controlling the target system. By integrating the self-adaptive sliding mode control theory and the PD control strategy, efficient and stable control over a complex dynamic system is achieved, the problem that in a traditional control method, the control effect is poor when the traditional control method faces nonlinearity, uncertainty and external disturbance is solved, and the response speed and robustness of the control system are improved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a control method, system and medium based on a sliding mode controller and a PD controller. Background Art

[0002] In the context of the rapid development of industrial automation and robotics, control algorithms have become an indispensable part of ensuring system performance, efficiency and reliability. However, traditional control methods have exposed several significant limitations in practical applications. For example, the problem of dynamic response hysteresis is common, that is, traditional control algorithms are difficult to quickly adapt to the rapid changes in the environment or task requirements, resulting in increased operation delays in the controlled system and possible oscillation, affecting the overall operation smoothness. At the same time, in terms of error prediction, traditional control algorithms are unable to handle nonlinear controlled systems and lack flexibility and real-time adjustment capabilities, which not only reduces the prediction accuracy, but also makes the controlled system more prone to deviations when facing complex working conditions. In addition, the traditional control method has insufficient adaptive ability and cannot effectively adjust parameters according to real-time feedback in a multi-variable and complex environment, which easily leads to overshoot or undershoot problems, further weakening the stability and response speed of the controlled system. In addition, the parameter update mechanism of traditional control methods usually relies on offline analysis and lacks online optimization functions, which means that once the controlled system is put into operation, its performance is difficult to be continuously improved. The existence of these problems may lead to reduced efficiency and stability of the controlled system, and even cause failures, limiting the development potential of industrial automation and robotics.

[0003] Therefore, the prior art still needs to be improved and enhanced. Summary of the invention

[0004] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a control method, system and medium based on a sliding mode controller and a PD controller are provided, aiming to solve the problems of dynamic response hysteresis, insufficient prediction of nonlinear system errors, lack of adaptive capability and dependence of parameter update mechanism on offline analysis in traditional control methods.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a control method based on a sliding mode controller and a PD controller, wherein the method comprises: Obtaining a target deviation value, and based on the target deviation value, obtaining a gain coefficient and a bias coefficient of the target system; Based on the gain coefficient and the bias coefficient, initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller are obtained; Based on the initial parameters and the initial sliding surface, control parameters are obtained, and the target system is controlled.

[0006] In one implementation, obtaining a gain coefficient and a bias coefficient of a target system based on the target deviation value includes: Acquire the data of the target deviation value within a preset time period to obtain a historical deviation value; Smoothing the target deviation value and the historical deviation value to obtain a processed deviation value, wherein the smoothing process includes but is not limited to an extended Kalman filter algorithm; Parameter identification is performed on the processed deviation value to obtain a gain coefficient and a bias coefficient of the target system.

[0007] In one implementation, obtaining initial parameters of an adaptive PD controller and an initial sliding surface of an adaptive sliding mode controller based on the gain coefficient and the bias coefficient includes: Based on the gain coefficient and the bias coefficient, constructing a system model of the target system; Based on the system model, initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller are obtained.

[0008] In one implementation, obtaining the control parameter based on the initial parameter and the initial sliding surface includes: Based on the system model, obtaining an expected deviation value; Based on the expected deviation value and the initial sliding surface, obtaining a target sliding surface of the adaptive sliding mode controller; Based on the expected deviation value, the target sliding surface and the initial parameters, the target parameters of the adaptive PD controller are calculated to obtain control parameters.

[0009] In one implementation, obtaining a target sliding surface of the adaptive sliding mode controller based on the expected deviation value and the initial sliding surface includes: Based on the initial slip surface, obtaining a threshold range of the initial slip surface; If the expected deviation value is within the threshold range, the initial sliding surface is updated to obtain an updated sliding surface, and based on the updated sliding surface, a target sliding surface of the adaptive sliding mode controller is obtained; If the expected deviation value is not within the threshold range, a target sliding surface of the adaptive sliding mode controller is obtained based on the initial sliding surface.

[0010] In one implementation, the target parameters of the adaptive PD controller are calculated based on the expected deviation value, the target sliding surface and the initial parameters to obtain the control parameters, including: Based on the target slip surface, obtaining a threshold of the target slip surface to obtain a target threshold; Based on the target threshold, the expected deviation value and the initial parameter, the target parameter of the adaptive PD controller is calculated to obtain the control parameter.

[0011] In one implementation, obtaining the target threshold value then includes: Based on the target threshold and the expected deviation value, obtaining an adaptive damping coefficient; Based on the adaptive damping coefficient, parameters of the adaptive sliding mode controller are adjusted.

[0012] In a second aspect, an embodiment of the present invention further provides a control system based on a sliding mode controller and a PD controller, wherein the system comprises: A gain coefficient and bias coefficient acquisition module, used to acquire a target deviation value, and based on the target deviation value, obtain a gain coefficient and a bias coefficient of a target system; An initial parameter and initial sliding surface acquisition module, used to obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller based on the gain coefficient and the bias coefficient; A control module is used to obtain control parameters based on the initial parameters and the initial sliding surface, and to control the target system.

[0013] In the third aspect, an embodiment of the present invention further provides a terminal, wherein the terminal includes a memory, a processor, and a control program based on a sliding mode controller and a PD controller stored in the memory and executable on the processor, and when the processor executes the control program based on the sliding mode controller and the PD controller, the steps of the control method based on the sliding mode controller and the PD controller of any one of the above-mentioned schemes are implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein a control program based on a sliding mode controller and a PD controller is stored on the computer-readable storage medium, and when the control program based on the sliding mode controller and the PD controller is executed by a processor, the steps of the control method based on the sliding mode controller and the PD controller described in any one of the above-mentioned schemes are implemented.

[0015] Beneficial effects: The present invention provides a control method based on a sliding mode controller and a PD controller. Compared with the prior art, the present invention first obtains a target deviation value, and based on the target deviation value, obtains a gain coefficient and a bias coefficient of the target system, ensuring that the control mechanism can dynamically respond to internal changes and external disturbances of the target system, solving the slow response problem caused by traditional fixed parameter settings, and enhancing the robustness and adaptability of the entire control system. Then, based on the gain coefficient and the bias coefficient, the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller are obtained, achieving accurate initialization of the controller parameters, avoiding the uncertainty and inefficiency caused by manual parameter adjustment, simplifying the debugging process and reducing the requirements for operator experience. Then, based on the initial parameters and the initial sliding surface, the control parameters are obtained, and the target system is controlled, ensuring the effectiveness and accuracy of the control strategy, so that the entire control system can perform well under various working conditions, and improving the control accuracy and smoothness. The method proposed in the present invention not only overcomes the limitations of traditional control systems by dynamically adjusting the controller parameters, but also improves the stability and response speed of the control system, and is suitable for a wide range of industrial application scenarios, such as automated production, robotics, aerospace, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a specific implementation of a control method based on a sliding mode controller and a PD controller provided in an embodiment of the present invention.

[0017] Figure 2 A flow chart of obtaining initial parameters of an adaptive PD controller and an initial sliding surface of an adaptive sliding mode controller in a control method based on a sliding mode controller and a PD controller provided in an embodiment of the present invention.

[0018] Figure 3 A flow chart of obtaining control parameters based on the initial parameters and the initial sliding surface and controlling the target system in a control method based on a sliding mode controller and a PD controller provided in an embodiment of the present invention.

[0019] Figure 4 It is a principle block diagram of a control system based on a sliding mode controller and a PD controller provided in an embodiment of the present invention.

[0020] Figure 5 This is a block diagram of the internal structure principle of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] In the context of the rapid development of industrial automation and robotics, control algorithms have become an indispensable part of ensuring system performance, efficiency and reliability. However, traditional control methods have exposed several significant limitations in practical applications. For example, the problem of dynamic response hysteresis is common, that is, traditional control algorithms are difficult to quickly adapt to the rapid changes in the environment or task requirements, resulting in increased operation delays in the controlled system and possible oscillation, affecting the overall operation smoothness. At the same time, in terms of error prediction, traditional control algorithms are unable to handle nonlinear controlled systems and lack flexibility and real-time adjustment capabilities, which not only reduces the prediction accuracy, but also makes the controlled system more prone to deviations when facing complex working conditions. In addition, the traditional control method has insufficient adaptive ability and cannot effectively adjust parameters according to real-time feedback in a multi-variable and complex environment, which easily leads to overshoot or undershoot problems, further weakening the stability and response speed of the controlled system. In addition, the parameter update mechanism of traditional control methods usually relies on offline analysis and lacks online optimization functions, which means that once the controlled system is put into operation, its performance is difficult to be continuously improved. The existence of these problems may lead to reduced efficiency and stability of the controlled system, and even cause failures, limiting the development potential of industrial automation and robotics.

[0023] In order to solve the above problems, this embodiment provides a control method based on a sliding mode controller and a PD controller. In specific implementation, this embodiment first obtains a target deviation value, and based on the target deviation value, obtains a gain coefficient and a bias coefficient of the target system, ensuring that the control mechanism can dynamically respond to internal changes and external disturbances of the target system, solves the slow response problem caused by traditional fixed parameter settings, and enhances the robustness and adaptability of the entire control system. Then, based on the gain coefficient and the bias coefficient, the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller are obtained, and the precise initialization of the controller parameters is achieved, avoiding the uncertainty and inefficiency caused by manual parameter adjustment, simplifying the debugging process and reducing the requirements for operator experience. Then, based on the initial parameters and the initial sliding surface, the control parameters are obtained, and the target system is controlled, ensuring the effectiveness and accuracy of the control strategy, so that the entire control system can perform well under various working conditions, and improving the control accuracy and smoothness. The method proposed in the present invention not only overcomes the limitations of traditional control systems by dynamically adjusting the controller parameters, but also improves the stability and response speed of the control system, and is suitable for a wide range of industrial application scenarios, such as automated production, robotics, aerospace, etc.

[0024] For example, suppose an automated factory wants to introduce a more accurate and adaptive control strategy for the robot control system on its production line. The method proposed in the present invention can be used. First, the target deviation value is obtained. These deviation values ​​come from the difference between the ideal path and the actual path of the robot when performing a specific task. This process provides data that directly reflects the performance of the system for the controller design, ensuring that it can effectively cope with challenges under various working conditions. The analysis of these deviation data not only helps to identify the deficiencies in the current control strategy, but also points out the direction for improvement measures, thereby ensuring that the designed controller has wide applicability and high efficiency. Next, based on the target deviation value, the gain coefficient and bias coefficient of the target system are calculated, so that engineers can adjust the control algorithm in a targeted manner, improve the response speed and accuracy of the system, and compensate for static errors. This step enhances the sensitivity and adaptability of the control system to changes in different working conditions, thereby improving the stability and reliability of the overall operation. This precise parameter setting method reduces the cost of trial and error and speeds up the development process. Then, the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller are determined by using the gain coefficient and bias coefficient obtained above. This step not only provides a starting point for achieving more accurate and stable control, but also simplifies the subsequent parameter adjustment process. The adaptive PD controller can dynamically adjust the proportional, integral and differential effects according to real-time feedback, while the sliding mode control introduces a nonlinear control strategy to deal with uncertainty and external interference. The combination of the two provides a solution to the control problem in a complex environment. Subsequently, based on the initial parameters and the initial sliding surface, the control parameters are obtained. In this process, engineers will continuously optimize the control parameters through experimental verification and iterative adjustment until the ideal control effect is achieved. This method ensures that even in a complex and changeable working environment, the robot can maintain high-performance operation and can quickly respond to changes in external conditions to ensure the continuity of the production process and the consistency of product quality. Finally, the obtained control parameters are applied to the target system to finely control the tasks performed by the robot. Due to the combination of adaptive PD and sliding mode control, not only the control accuracy is improved, but also the robustness and adaptability of the system are enhanced. This means that the robot can maintain a high level of operating performance in the face of complex situations such as load changes and temperature fluctuations, and to a great extent avoids product defects or production line stoppages caused by control errors. In summary, through the method proposed in the present invention, automated factories can significantly improve production efficiency and product quality, while also reducing maintenance costs and time, providing strong technical support for the development of Industry 4.0.

[0025] The control method based on the sliding mode controller and the PD controller provided in this embodiment can be applied to intelligent terminals, such as Figure 1 As shown in , the specific steps include: Step S100: Obtain a target deviation value, and based on the target deviation value, obtain a gain coefficient and a bias coefficient of a target system.

[0026] In this embodiment, if Figure 2 As shown in the figure, first, the error value observed by the sensor is read. This error value refers to the difference between the actual output measured by the sensor and the set target value (i.e., the expected set value or reference value). By obtaining these error values, specific deviation information about the current value of the target system relative to the target value can be obtained. This step is the basis for all subsequent calculations and adjustments because it provides the most direct feedback, enabling the control system to instantly understand and respond to any performance gap. Next, the target deviation value is calculated based on the obtained error value. The target deviation value refers to a comprehensive indicator that reflects the current performance gap and the degree of deviation from the ideal behavior of the target system after processing and calculation based on the error value observed by the sensor and other related information. That is, the target deviation value refers to the difference between the target state (expected final state) of the set target system and the state of the target system actually measured. The purpose of calculating the target deviation is to provide a quantitative indicator to guide subsequent parameter adjustments, ensure that the control system can respond in a targeted manner, and improve the response speed and accuracy. Then, based on the target deviation value, the gain coefficient and bias coefficient of the target system are obtained. The gain coefficient determines the sensitivity of the controller to input changes, while the bias coefficient is used to correct static errors. The accurate calculation of these two coefficients is crucial to optimizing control performance. By dynamically adjusting these coefficients, the control system can better adapt to changes in internal parameters or external disturbances, significantly enhancing its robustness and adaptability. For example, in the control system of a wind turbine, real-time updating of gain and bias coefficients can enable the generator to maintain stable and efficient power generation efficiency even when the wind speed changes. This adaptive mechanism not only solves the problem of slow response caused by traditional fixed parameter settings, but also improves the stability and response speed of the entire control system, ensuring that it can perform well under various working conditions, and improving control accuracy and smoothness. In summary, the present invention ensures the efficient operation of the control system through a series of carefully designed steps from obtaining error values ​​to calculating target deviations and then determining gain coefficients and bias coefficients. These steps work together to not only enhance the robustness and adaptability of the system, simplify the debugging process and reduce the requirements for operator experience, but also overcome the limitations of traditional control systems, providing a more intelligent, flexible and accurate solution for industrial applications.

[0027] Specifically, the step S100 includes the following steps: Step S101, acquiring the target deviation value data within a preset time period to obtain a historical deviation value; Step S102, smoothing the target deviation value and the historical deviation value to obtain a processed deviation value, wherein the smoothing process includes but is not limited to an extended Kalman filter algorithm; Step S103: performing parameter identification on the processed deviation value to obtain a gain coefficient and a bias coefficient of the target system.

[0028] In one implementation, after obtaining the target deviation value, as Figure 2 As shown, first, the data of the target deviation value within a preset time period is obtained to obtain the historical deviation value, which provides the necessary data basis for subsequent analysis. This process not only helps to understand the past behavior of the target system, but also provides a reference for predicting future trends. Then, in order to reduce the influence of noise interference and abnormal data points on the analysis results, the target deviation value and the historical deviation value are smoothed to obtain the processed deviation value, and the smoothing process includes but is not limited to the extended Kalman filter algorithm. The extended Kalman filter algorithm is a recursive estimation method suitable for the state estimation problem of nonlinear dynamic systems. It can effectively estimate the state of the system by iteratively updating the two steps of prediction and measurement in the presence of uncertainty. The advantage of smoothing is that it can improve the signal quality and reduce the impact of random fluctuations, thereby obtaining a more accurate and stable deviation value representation. For example, in the position control of an autonomous vehicle, the use of the extended Kalman filter algorithm can improve the accuracy of the position information and ensure that the vehicle travels along the predetermined path. Then, the processed deviation value is parameter identified to obtain the gain coefficient and bias coefficient of the target system. The gain coefficient determines the intensity of the system response, while the bias coefficient reflects the static output level of the system in the absence of input. Parameter identification is achieved through a series of mathematical model fitting or machine learning algorithms that can find the parameter combination that best represents the behavior of the target system from a large amount of data. Accurate identification of these two coefficients is crucial for optimizing the control strategy because it can help adjust the controller parameters to adapt to different operating conditions, thereby improving the response speed and stability of the target system. For example, in an industrial automation environment, the correct parameter setting can enable a robot arm to perform tasks more accurately, reduce material waste and improve production efficiency. The entire process is executed in the order of acquisition, smoothing and then parameter identification. Each link is closely linked and works together to ultimately achieve the purpose of improving the performance of the control system, ensuring that every step from raw data to optimized control decisions is fully considered and scientifically processed, providing a solid foundation for achieving efficient and stable control.

[0029] Step S200: Based on the gain coefficient and the bias coefficient, initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller are obtained.

[0030] In this embodiment, after obtaining the gain coefficient and the bias coefficient, the initial parameters of the adaptive PD (Proportional-Derivative) controller are obtained based on the gain coefficient and the bias coefficient, so that the adaptive PD controller can exert appropriate control force on the target system according to the current error situation, thereby achieving fast response and high-precision tracking, which helps to improve the adaptability and flexibility of the adaptive PD controller, so that it can better match the dynamic characteristics of the target system, reduce overshoot and speed up the response speed. In addition, the initial sliding surface of the adaptive sliding mode controller is also obtained based on the gain coefficient and the bias coefficient, so as to enhance the anti-interference ability of the target system and simplify the design process of the adaptive sliding mode controller. In summary, after obtaining the gain coefficient and the bias coefficient, setting the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller accordingly is crucial to ensure the efficient operation of the control system. This method not only improves the control accuracy and dynamic response, but also enhances the robustness of the target system in the face of uncertainty and interference.

[0031] Specifically, the step S200 includes the following steps: Step S201: constructing a system model of the target system based on the gain coefficient and the bias coefficient; Step S202: Based on the system model, initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller are obtained.

[0032] In one implementation, after obtaining the gain coefficient and the bias coefficient, as follows Figure 2As shown, first, a suitable mathematical model form is selected to describe the behavior of the target system. For example, for a linear time-invariant system, a transfer function or a state space model can be used for modeling. Then, based on the gain coefficient and the bias coefficient, as well as the selected mathematical model, a system model of the target system is constructed. The target system is the controlled system, that is, the system that needs to be controlled. The system model is a mathematical model that can accurately reflect the behavior of the target system. This model not only includes the linear part of the target system, but may also cover nonlinear factors and time-varying characteristics, thereby providing a solid foundation for the subsequent design of an adaptive PD controller and an adaptive sliding mode controller. Then, based on the system model, the initial parameters of the adaptive PD controller are obtained. The adaptive PD controller is one of the most commonly used feedback controllers in the industry and is widely popular because of its simple structure, easy understanding and implementation. However, the parameters of traditional adaptive PD controllers are usually fixed, which may not be able to cope with changes in working conditions or system uncertainties. Therefore, it is significantly advantageous to use an adaptive method to adjust the PD parameters. It can continuously optimize the control effect based on real-time performance evaluation to ensure that good control quality is maintained even when the operating environment changes. For example, in a chemical process control scenario, when the concentration of reactants fluctuates, the adaptive PD controller can respond quickly to such changes and automatically adjust its parameters to maintain stable product quality. At the same time, based on the system model, the initial sliding surface of the adaptive sliding mode controller is obtained. Sliding mode control is a nonlinear control technology known for its strong robustness to parameter uncertainty and external disturbances. The design of the sliding surface determines the convergence speed and stability of the target system. A good sliding surface can make the state of the target system quickly approach the ideal trajectory, and once it enters the sliding mode, it can resist the influence of external interference to a large extent. For applications such as aircraft attitude control that require high precision and fast response, the adaptive sliding mode controller can achieve more stable and precise control by dynamically adjusting the sliding surface parameters, while enhancing the anti-interference ability of the target system. In summary, by combining the gain coefficient and the bias coefficient to construct the system model, and setting the initial parameters and sliding surface of the adaptive PD controller and the adaptive sliding mode controller accordingly, not only the flexibility and adaptability of the control system are improved, but also the reliability and performance in the face of complex and variable working conditions are greatly enhanced. This method inherits the advantages of classical control theory and incorporates the advantages of modern intelligent algorithms, providing an efficient and reliable solution to practical engineering problems.

[0033] Step S300: Based on the initial parameters and the initial sliding surface, a control parameter is obtained, and the target system is controlled.

[0034] In this embodiment, after obtaining the initial parameters and the initial sliding surface, first, based on the initial parameters and the initial sliding surface, the control parameters are obtained to ensure that the selected control parameters can make the response of the target system as close as possible to the ideal sliding mode, thereby achieving a high-performance control effect. Then, based on the control parameters, the target system is controlled to provide a reliable guarantee for the stable operation of the target system.

[0035] Specifically, step S300 includes the following steps: Step S301, obtaining an expected deviation value based on the system model; Step S302, obtaining a target sliding surface of the adaptive sliding mode controller based on the expected deviation value and the initial sliding surface; Step S303: Calculate target parameters of the adaptive PD controller based on the expected deviation value, the target sliding surface and the initial parameters to obtain control parameters.

[0036] In one implementation, after obtaining the initial parameters and the initial sliding surface, as Figure 3As shown, first, based on the system model, the expected deviation value is obtained, and the expected deviation value refers to the difference between the output predicted by the system model and the expected ideal output. Then, based on the initial sliding surface, the threshold range of the initial sliding surface is obtained; it is determined whether the expected deviation value is within the threshold range. If the expected deviation value is within the threshold range, the initial sliding surface is updated to obtain an updated sliding surface, and the target sliding surface of the adaptive sliding mode controller is obtained based on the updated sliding surface; if the expected deviation value is not within the threshold range, the target sliding surface of the adaptive sliding mode controller is obtained based on the initial sliding surface. The sliding surface is a core concept in sliding mode control theory. It defines an ideal trajectory along which the target system state should evolve to achieve stable or desired behavior. Setting the threshold range helps to define the allowable deviation range, ensuring that the target system response is neither too aggressive nor too conservative, and ensuring that the control system can respond quickly and correct large deviations. After obtaining the target sliding surface of the adaptive sliding mode controller, first, based on the target sliding surface, the threshold of the target sliding surface is obtained to obtain the target threshold. This step is crucial to understanding the extent to which the target system may deviate from the ideal path, because it is directly related to how to adjust the adaptive sliding mode controller to cope with these deviations. Next, based on the target threshold and the expected deviation value, the adaptive damping coefficient is obtained. The adaptive damping coefficient is a key parameter for adjusting the damping effect in the control system, which determines the response characteristics of the system to input changes. In order to calculate this coefficient, the position of the expected deviation value relative to the target threshold is considered: if the deviation is large, it means that the target system is far away from the ideal sliding surface. At this time, it may be necessary to increase the damping to quickly reduce the deviation; conversely, if the deviation is small and close to or within the target threshold range, a lower damping coefficient can be selected to maintain stability without introducing unnecessary slow response. Specifically, the calculation of the adaptive damping coefficient D can be achieved by a predefined function f(expected deviation value, target threshold), which converts the relationship between the expected deviation value and the target threshold into a specific damping coefficient. For example, in some applications, a piecewise linear function can be used to increase damping rapidly when the deviation exceeds a critical point, while maintaining a relatively gentle change in other cases. Then, based on the calculated adaptive damping coefficient, the damping response mode of the target system is determined to optimize the dynamic performance of the target system. The damping response mode is divided into three categories: underdamping, critical damping, and overdamping, which determine the response speed and stability of the target system to the input signal. Specifically, based on the calculated adaptive damping coefficient D, the most appropriate damping response mode is determined by comparing it with a preset standard or reference value.For example, if D is close to zero, it indicates that the target system should be in an underdamped state, allowing a fast response but possibly accompanied by oscillations; if D reaches a certain critical value, it indicates that the target system should be in a critical damped state, providing the fastest response without overshoot; if D is large, it indicates that the target system should be in an overdamped state, ensuring stability but with a slow response speed. Subsequently, according to the determined damping response mode, the parameters of the adaptive sliding mode controller are adjusted to ensure that the control system can still maintain good dynamic performance in the face of uncertain factors. Considering that sliding mode control sometimes introduces chattering, that is, high-frequency oscillations, which may cause damage to actual hardware. In order to alleviate this problem, an adaptive PD controller is introduced. Based on the target threshold, the expected deviation value and the initial parameters, the target parameters of the adaptive PD controller are calculated to obtain the control parameters. This method not only inherits the advantages of sliding mode control, such as robustness to external disturbances and parameter uncertainties, but also takes advantage of the characteristics of the adaptive PD controller that is easy to understand and implement, thereby improving the overall control quality and reducing the occurrence of chattering. Finally, based on the control parameters, the control of the target system is realized. Specifically, all the control parameters calculated and adjusted above are passed to the motion planner as actual input. The motion planner plans a specific motion trajectory or action sequence according to these control parameters, and generates corresponding execution instructions, which are finally applied to the target system through the actuator to achieve precise control of its behavior. This final step integrates all the above optimizations and adjustments to ensure that the target system can not only respond to changes quickly and accurately, but also maintain a high degree of stability and robustness, thereby achieving optimal control performance. In summary, this embodiment first obtains the target deviation value, and based on the target deviation value, obtains the gain coefficient and bias coefficient of the target system, ensuring that the control mechanism can dynamically respond to internal changes and external disturbances of the target system, solves the slow response problem caused by traditional fixed parameter settings, and enhances the robustness and adaptability of the entire control system. Then, based on the gain coefficient and the bias coefficient, the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller are obtained, which realizes the precise initialization of the controller parameters, avoids the uncertainty and inefficiency caused by manual parameter adjustment, simplifies the debugging process and reduces the requirements for operator experience. Then, based on the initial parameters and the initial sliding surface, the control parameters are obtained, and the target system is controlled, thereby ensuring the effectiveness and accuracy of the control strategy, so that the entire control system can perform well under various working conditions, and the control accuracy and smoothness are improved. The method proposed in the present invention not only overcomes the limitations of traditional control systems by dynamically adjusting the controller parameters, but also improves the stability and response speed of the control system, and is suitable for a wide range of industrial application scenarios, such as automated production, robotics, aerospace, etc.

[0037] like Figure 4As shown in , this embodiment also provides a control system based on a sliding mode controller and a PD controller, the system comprising: a gain coefficient and bias coefficient acquisition module 10, an initial parameter and initial sliding surface acquisition module 20 and a control module 30. Specifically, the gain coefficient and bias coefficient acquisition module 10 is used to acquire a target deviation value, and based on the target deviation value, obtain the gain coefficient and bias coefficient of the target system. The initial parameter and initial sliding surface acquisition module 20 is used to obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller based on the gain coefficient and the bias coefficient. The control module 30 is used to obtain control parameters based on the initial parameters and the initial sliding surface, and control the target system.

[0038] In one implementation, the gain coefficient and bias coefficient acquisition module 10 includes: A historical deviation value acquisition unit, used to acquire deviation value data within a preset time period to obtain a historical deviation value; A smoothing processing unit, used for smoothing the target deviation value and the historical deviation value to obtain a processed deviation value, wherein the smoothing processing includes but is not limited to an extended Kalman filter algorithm; The gain coefficient and bias coefficient acquisition unit is used to perform parameter identification on the processed deviation value to obtain the gain coefficient and bias coefficient of the target system.

[0039] In one implementation, the initial parameter and initial sliding surface acquisition module 20 includes: A system model building unit, used to build a system model of the target system based on the gain coefficient and the bias coefficient; The initial parameter and initial sliding surface acquisition unit is used to obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller based on the system model.

[0040] In one implementation, the control module 30 includes: An expected deviation value acquisition unit, used for acquiring an expected deviation value based on the system model; a target sliding surface acquisition unit, configured to obtain a target sliding surface of the adaptive sliding mode controller based on the expected deviation value and the initial sliding surface; A control parameter acquisition unit is used to calculate the target parameters of the adaptive PD controller based on the expected deviation value, the target sliding surface and the initial parameters to obtain control parameters.

[0041] In one implementation, the target sliding surface acquisition unit includes: A threshold range acquisition first subunit is used to obtain a threshold range of the initial slip surface based on the initial slip surface; A target sliding surface acquisition first subunit is used to update the initial sliding surface to obtain an updated sliding surface if the expected deviation value is within the threshold range, and obtain a target sliding surface of the adaptive sliding mode controller based on the updated sliding surface; The target sliding surface acquisition second subunit is used to obtain the target sliding surface of the adaptive sliding mode controller based on the initial sliding surface if the expected deviation value is not within the threshold range.

[0042] In one implementation, the control parameter acquisition unit includes: A target threshold value acquisition subunit is used to acquire a threshold value of the target slip surface based on the target slip surface to obtain a target threshold value; The control parameter acquisition subunit is used to calculate the target parameter of the adaptive PD controller based on the target threshold, the expected deviation value and the initial parameter to obtain the control parameter.

[0043] In one implementation, obtaining the target threshold value then includes: An adaptive damping coefficient acquisition subunit, configured to obtain an adaptive damping coefficient based on the target threshold and the expected deviation value; The parameter adjustment subunit is used to adjust the parameters of the adaptive sliding mode controller based on the adaptive damping coefficient.

[0044] The working principles of each module in the control system based on the sliding mode controller and the PD controller in this embodiment are the same as the principles of each step in the above method embodiment, and will not be repeated here.

[0045] Based on the above embodiment, the present invention further provides a terminal, the principle block diagram of the terminal can be as follows: Figure 5 The terminal may include one or more processors 100 ( Figure 5 Only one is shown in the figure), a memory 101 and a computer program 102 stored in the memory 101 and executable on one or more processors 100, for example, a control program based on a sliding mode controller and a PD controller. When one or more processors 100 execute the computer program 102, the various steps in the control method embodiment based on the sliding mode controller and the PD controller can be implemented. Alternatively, when one or more processors 100 execute the computer program 102, the functions of each module / unit in the control method embodiment based on the sliding mode controller and the PD controller can be implemented, which is not limited here.

[0046] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0047] In one embodiment, the memory 101 may be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 101 may also include both an internal storage unit of the electronic device and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0048] Those skilled in the art will understand that Figure 5 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal to which the scheme of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0049] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, operating database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double operational data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method based on a sliding mode controller and a PD controller, characterized in that: The method comprises: Obtaining a target deviation value, and based on the target deviation value, obtaining a gain coefficient and a bias coefficient of the target system; Based on the gain coefficient and the bias coefficient, initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller are obtained; Based on the initial parameters and the initial sliding surface, control parameters are obtained, and the target system is controlled.

2. The control method based on sliding mode controller and PD controller according to claim 1, characterized in that: The step of obtaining a gain coefficient and a bias coefficient of a target system based on the target deviation value includes: Acquire the data of the target deviation value within a preset time period to obtain a historical deviation value; Smoothing the target deviation value and the historical deviation value to obtain a processed deviation value, wherein the smoothing process includes but is not limited to an extended Kalman filter algorithm; Parameter identification is performed on the processed deviation value to obtain a gain coefficient and a bias coefficient of the target system.

3. The control method based on sliding mode controller and PD controller according to claim 2 is characterized in that: The step of obtaining initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller based on the gain coefficient and the bias coefficient includes: Based on the gain coefficient and the bias coefficient, constructing a system model of the target system; Based on the system model, initial parameters of the adaptive PD controller and an initial sliding surface of the adaptive sliding mode controller are obtained.

4. The control method based on sliding mode controller and PD controller according to claim 3 is characterized in that: The obtaining of control parameters based on the initial parameters and the initial sliding surface comprises: Based on the system model, obtaining an expected deviation value; Based on the expected deviation value and the initial sliding surface, obtaining a target sliding surface of the adaptive sliding mode controller; Based on the expected deviation value, the target sliding surface and the initial parameters, the target parameters of the adaptive PD controller are calculated to obtain control parameters.

5. The control method based on sliding mode controller and PD controller according to claim 4 is characterized in that: The step of obtaining a target sliding surface of the adaptive sliding mode controller based on the expected deviation value and the initial sliding surface comprises: Based on the initial slip surface, obtaining a threshold range of the initial slip surface; If the expected deviation value is within the threshold range, the initial sliding surface is updated to obtain an updated sliding surface, and based on the updated sliding surface, a target sliding surface of the adaptive sliding mode controller is obtained; If the expected deviation value is not within the threshold range, a target sliding surface of the adaptive sliding mode controller is obtained based on the initial sliding surface.

6. The control method based on sliding mode controller and PD controller according to claim 5, characterized in that: The step of calculating the target parameters of the adaptive PD controller based on the expected deviation value, the target sliding surface and the initial parameters to obtain the control parameters includes: Based on the target slip surface, obtaining a threshold of the target slip surface to obtain a target threshold; Based on the target threshold, the expected deviation value and the initial parameter, the target parameter of the adaptive PD controller is calculated to obtain the control parameter.

7. The control method based on sliding mode controller and PD controller according to claim 6, characterized in that: The step of obtaining the target threshold value further comprises: Based on the target threshold and the expected deviation value, obtaining an adaptive damping coefficient; Based on the adaptive damping coefficient, parameters of the adaptive sliding mode controller are adjusted.

8. A control system based on a sliding mode controller and a PD controller, characterized in that: The system comprises: A gain coefficient and bias coefficient acquisition module, used to acquire a target deviation value, and based on the target deviation value, obtain a gain coefficient and a bias coefficient of a target system; An initial parameter and initial sliding surface acquisition module, used to obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller based on the gain coefficient and the bias coefficient; A control module is used to obtain control parameters based on the initial parameters and the initial sliding surface, and to control the target system.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and a control program based on a sliding mode controller and a PD controller stored in the memory and executable on the processor. When the processor executes the control program based on the sliding mode controller and the PD controller, the steps of the control method based on the sliding mode controller and the PD controller as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a control program based on a sliding mode controller and a PD controller. When the control program based on a sliding mode controller and a PD controller is executed by a processor, the steps of the control method based on a sliding mode controller and a PD controller as described in any one of claims 1 to 7 are implemented.

Images