Control Method, System and Medium Based on Sliding Mode Controller and PD Controller

Abstract

The present invention discloses a control method, system and medium based on a sliding mode controller and a PD controller. The method includes: obtaining a target deviation value, and based on the target deviation value, obtaining a gain coefficient and a bias coefficient of a target system; based on the gain coefficient and the bias coefficient, obtaining initial parameters of an adaptive PD controller and an initial sliding surface of an adaptive sliding mode controller; based on the initial parameters and the initial sliding surface, obtaining control parameters and controlling the target system. By integrating the adaptive sliding mode control theory and the PD control strategy, the present invention realizes efficient and stable control of complex dynamic systems, solves the problem of poor control effect in traditional control methods when facing non-linearity, uncertainty and external disturbances, and improves the response speed and robustness of the control system.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control, and particularly 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 revealed several significant limitations in practical applications. For example, the problem of dynamic response hysteresis is widespread, that is, traditional control algorithms are difficult to quickly adapt to rapid changes in the environment or task requirements, resulting in an increase in the operation delay of the controlled system and possible oscillation phenomena, which affect the overall operation fluency. At the same time, in terms of error prediction, traditional control algorithms are unable to handle non-linear controlled systems effectively, lacking flexibility and real-time adjustment capabilities. This not only reduces the prediction accuracy, but also makes the controlled system more prone to deviation in the face of complex working conditions. In addition, the adaptive ability of traditional control methods is insufficient, and they cannot effectively adjust parameters according to real-time feedback in a multi-variable and complex environment, thus easily causing overshoot or undershoot problems, further weakening the stability and response speed of the controlled system. Moreover, 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 a decrease in the efficiency and stability of the controlled system, and even cause failures, restricting the development potential of industrial automation and robotics.

[0003] Therefore, the existing technology still needs to be improved and enhanced. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a control method, system and medium based on a sliding mode controller and a PD controller in view of the above-mentioned defects of the existing technology, aiming to solve the problems of dynamic response hysteresis, insufficient non-linear system error prediction, lack of adaptive ability and parameter update mechanism relying on offline analysis in traditional control methods.

[0005] To solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0006] In the first aspect, the present invention provides a control method based on a sliding mode controller and a PD controller, wherein the method includes:

[0007] Obtain a target deviation value, and based on the target deviation value, obtain the gain coefficient and bias coefficient of the target system;

[0008] Based on the gain coefficient and the bias coefficient, obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller;

[0009] Based on the initial parameters and the initial sliding surface, control parameters are obtained, and the target system is controlled.

[0010] In one implementation, the obtaining of the gain coefficient and the bias coefficient of the target system based on the target deviation value includes:

[0011] Obtain the data of the target deviation value within a preset time period to obtain a historical deviation value;

[0012] Smooth the target deviation value and the historical deviation value to obtain a processed deviation value, and the smoothing process includes but is not limited to the extended Kalman filter algorithm;

[0013] Perform parameter identification on the processed deviation value to obtain the gain coefficient and the bias coefficient of the target system.

[0014] In one implementation, the obtaining of 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 includes:

[0015] Based on the gain coefficient and the bias coefficient, construct a system model of the target system;

[0016] Based on the system model, obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller.

[0017] In one implementation, the obtaining of the control parameters based on the initial parameters and the initial sliding surface includes:

[0018] Based on the system model, obtain an expected deviation value;

[0019] Based on the expected deviation value and the initial sliding surface, obtain the target sliding surface of the adaptive sliding mode controller;

[0020] Based on the expected deviation value, the target sliding surface and the initial parameters, calculate the target parameters of the adaptive PD controller to obtain control parameters.

[0021] In one implementation, the obtaining of the target sliding surface of the adaptive sliding mode controller based on the expected deviation value and the initial sliding surface includes:

[0022] Based on the initial sliding surface, obtain the threshold range of the initial sliding surface;

[0023] If the expected deviation value is within the threshold range, update the initial sliding surface to obtain an updated sliding surface, and based on the updated sliding surface, obtain the target sliding surface of the adaptive sliding mode controller;

[0024] 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.

[0025] In one implementation, 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 control parameters includes:

[0026] Based on the target sliding surface, a threshold of the target sliding surface is obtained to get a target threshold;

[0027] 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 control parameters.

[0028] In one implementation, after obtaining the target threshold, it includes:

[0029] Based on the target threshold and the expected deviation value, an adaptive damping coefficient is obtained;

[0030] Based on the adaptive damping coefficient, the parameters of the adaptive sliding mode controller are adjusted.

[0031] 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 includes:

[0032] A gain coefficient and bias coefficient acquisition module, configured 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;

[0033] An initial parameter and initial sliding surface acquisition module, configured to obtain an initial parameter 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;

[0034] A control module, configured to obtain control parameters based on the initial parameter and the initial sliding surface, and control the target system.

[0035] In a 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. 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 in any one of the above solutions are implemented.

[0036] Fourthly, an embodiment of the present invention further provides a computer-readable storage medium, on which a control program based on a sliding mode controller and a PD controller is stored. 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 solutions are implemented.

[0037] 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 problem of slow response 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, realizing the accurate initialization of the controller parameters, avoiding the uncertainty and low efficiency brought by manual parameter adjustment, simplifying the debugging process and reducing the requirements for the experience of operators. Then, based on the initial parameters and the initial sliding surface, control parameters are obtained, and the target system is controlled, ensuring the effectiveness and accuracy of the control strategy, enabling the entire control system to perform well under various working conditions, and improving the control accuracy and smoothness. The method proposed by 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 applicable to a wide range of industrial application scenarios, such as automated production, robotics, aerospace, etc. Description of the Drawings

[0038] Figure 1 It is a flowchart of the specific implementation manner of the control method based on the sliding mode controller and the PD controller provided by the embodiment of the present invention.

[0039] Figure 2 It is a flowchart of obtaining the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller in the control method based on the sliding mode controller and the PD controller provided by the embodiment of the present invention.

[0040] Figure 3 It is a flowchart of obtaining control parameters based on the initial parameters and the initial sliding surface and controlling the target system in the control method based on the sliding mode controller and the PD controller provided by the embodiment of the present invention.

[0041] Figure 4 It is a principle block diagram of the control system based on the sliding mode controller and the PD controller provided by the embodiment of the present invention.

[0042] Figure 5It is a block diagram of the internal structure principle of the terminal provided by the embodiment of the present invention. Detailed implementation manners

[0043] To make the objectives, technical solutions and effects of the present invention clearer and more definite, the following further describes the present invention in detail with reference to the accompanying drawings and by way of 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.

[0044] 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 revealed several significant limitations in practical applications. For example, the problem of dynamic response hysteresis is widespread, that is, traditional control algorithms are difficult to quickly adapt to the rapid changes in the environment or task requirements, resulting in an increase in the operation delay of the controlled system and possible oscillation phenomena, which affects the overall operation fluency. At the same time, in terms of error prediction, traditional control algorithms are unable to handle nonlinear controlled systems effectively, lacking flexibility and real-time adjustment capabilities. This not only reduces the prediction accuracy but also makes the controlled system more prone to deviation when facing complex working conditions. In addition, the traditional control methods have insufficient adaptive capabilities and cannot effectively adjust parameters according to real-time feedback in multivariable and complex environments, thus easily causing overshoot or undershoot problems, further weakening the stability and response speed of the controlled system. Moreover, the parameter update mechanism of traditional control methods usually relies on offline analysis and lacks an online optimization function, 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 a decrease in the efficiency and stability of the controlled system, and even cause failures, limiting the development potential of industrial automation and robotics.

[0045] To solve the above problems, this embodiment provides a control method based on a sliding mode controller and a PD controller. Specifically, in implementation, this embodiment first obtains a 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, solving the problem of slow response caused by traditional fixed parameter settings, and enhancing the robustness and adaptability of the entire control system. Next, 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, realizing the precise initialization of the controller parameters, avoiding the uncertainty and low efficiency brought by manual parameter adjustment, simplifying the debugging process and reducing the requirements for the operator's experience. Then, based on the initial parameters and the initial sliding surface, control parameters are obtained and the target system is controlled, ensuring the effectiveness and accuracy of the control strategy, enabling the entire control system to perform excellently 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 applicable to a wide range of industrial application scenarios, such as automated production, robotics, aerospace, etc.

[0046] 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.

[0047] 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:

[0048] Step S100: Obtain the target deviation value, and based on the target deviation value, obtain the gain coefficient and bias coefficient of the target system.

[0049] In this embodiment, as Figure 2 shown, first, read the error value observed by the sensor. This error value refers to the difference between the actual output measured by the sensor and the set target value (i.e., the desired 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, calculate the target deviation value based on the obtained error values. The target deviation value refers to a comprehensive index that reflects the current performance gap and the degree of deviation from the ideal behavior of the target system after being processed and calculated based on the error values observed by the sensor and other relevant information. That is, the target deviation value refers to the difference between the target state (desired final state) of the set target system and the state of the target system actually measured. The role of calculating the target deviation is to provide a quantitative index to guide subsequent parameter adjustments, ensuring that the control system can respond specifically, improving the response speed and accuracy. Then, based on the target deviation value, obtain the gain coefficient and bias coefficient of the target system. The gain coefficient determines the sensitivity of the controller to input changes, while the bias coefficient is used to correct static errors. The precise calculation of these two coefficients is crucial for 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 the gain and bias coefficients can enable the generator to maintain stable and efficient power generation even under changing wind speeds. 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 excellently under various working conditions, improving control accuracy and smoothness. In summary, through a series of carefully designed steps from obtaining error values to calculating the target deviation and then determining the gain coefficient and bias coefficient, the present invention ensures the efficient operation of the control system. These steps work together to not only enhance the robustness and adaptability of the system, simplify the debugging process and reduce the requirements for the operator's experience but also overcome the limitations in traditional control systems, providing a more intelligent, flexible, and accurate solution for industrial applications.

[0050] Specifically, step S100 includes the following steps:

[0051] Step S101: Obtain the data of the target deviation value within a preset time period to obtain the historical deviation value;

[0052] Step S102: Smooth the target deviation value and the historical deviation value to obtain a processed deviation value. The smoothing process includes, but is not limited to, the extended Kalman filter algorithm;

[0053] Step S103: Perform parameter identification on the processed deviation value to obtain the gain coefficient and bias coefficient of the target system.

[0054] In one implementation, after obtaining the target deviation value, as Figure 2 shown, first, obtain the data of the target deviation value within a preset time period to obtain the historical deviation value, providing a 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, smooth the target deviation value and the historical deviation value to obtain a processed deviation value. The smoothing process includes, but is not limited to, the extended Kalman filter algorithm. The extended Kalman filter algorithm is a recursive estimation method applicable to the state estimation problem of nonlinear dynamic systems. It can effectively estimate the state of the system by iteratively updating the prediction and measurement steps in the presence of uncertainty. The advantage of the smoothing process is that it can improve the signal quality and reduce the influence of random fluctuations, thereby obtaining a more accurate and stable representation of the deviation value. For example, in the position control of an autonomous vehicle, using the extended Kalman filter algorithm can improve the accuracy of the position information and ensure that the vehicle travels along the predetermined path. Then, perform parameter identification on the processed deviation value 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 without input. Parameter identification is achieved through a series of mathematical model fittings or machine learning algorithms, which can find the parameter combination that best represents the behavior of the target system from a large amount of data. Accurately identifying 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, correct parameter settings can enable the robotic arm to perform tasks more precisely, reduce material waste, and improve production efficiency. The entire process is executed in sequence according to the order of acquisition, smoothing process, and then parameter identification. Each link is closely connected and jointly acts to ultimately achieve the purpose of improving the performance of the control system, ensuring that every step from the original data to the optimized control decision is fully considered and scientifically processed, providing a solid foundation for achieving efficient and stable control.

[0055] Step S200: Based on the gain coefficient and the bias coefficient, obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller.

[0056] In this embodiment, after obtaining the gain coefficient and the bias coefficient, based on the gain coefficient and the bias coefficient, the initial parameters of the adaptive PD (Proportional-Derivative) controller are obtained, so that the adaptive PD controller can apply appropriate control force to the target system according to the current error situation, thereby achieving fast response and high-precision tracking, helping to improve the adaptability and flexibility of the adaptive PD controller, enabling it to better match the dynamic characteristics of the target system, reducing the overshoot and accelerating the response speed. Also, based on the gain coefficient and the bias coefficient, the initial sliding surface of the adaptive sliding mode controller is obtained 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, and setting the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller accordingly, it 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 uncertainties and disturbances.

[0057] Specifically, the step S200 includes the following steps:

[0058] Step S201, based on the gain coefficient and the bias coefficient, construct the system model of the target system;

[0059] Step S202, based on the system model, obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller.

[0060] In one implementation, after obtaining the gain coefficient and the bias coefficient, as Figure 2As shown, first, select an appropriate mathematical model form 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, construct the system model of the target system. 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 non-linear factors and time-varying characteristics, thus providing a solid foundation for the subsequent design of the adaptive PD controller and the adaptive sliding mode controller. Next, based on the system model, obtain the initial parameters of the adaptive PD controller. The adaptive PD controller is one of the most commonly used feedback controllers in the industrial field and is widely popular because of its simple structure, easy to understand and implement. However, the parameters of the traditional adaptive PD controller are usually fixed, which may not be able to cope with changes in working conditions or system uncertainties. Therefore, using an adaptive method to adjust the PD parameters has significant advantages. It can continuously optimize the control effect according to real-time performance evaluation, ensuring that good control quality can be maintained even when the operating environment changes. For example, in a chemical process control scenario, when the reactant concentration fluctuates, the adaptive PD controller can quickly respond to this change and automatically adjust its parameters to keep the product quality stable. At the same time, based on the system model, obtain the initial sliding surface of the adaptive sliding mode controller. Sliding mode control is a non-linear control technology known for its strong robustness to parameter uncertainties 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 largely resist the influence of external disturbances. For applications such as aircraft attitude control that require high precision and fast response, the adaptive sliding mode controller can achieve smoother and more accurate 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 changing working conditions are greatly enhanced. This method not only inherits the advantages of classical control theory but also incorporates the advantages of modern intelligent algorithms, providing an efficient and reliable solution for solving practical engineering problems.

[0061] Step S300: Based on the initial parameters and the initial sliding surface, obtain the control parameters and control the target system.

[0062] In this embodiment, after obtaining the initial parameters and the initial sliding surface, first, based on the initial parameters and the initial sliding surface, 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, so as to achieve a high-performance control effect. Then, based on the control parameters, the target system is controlled, providing a reliable guarantee for the stable operation of the target system.

[0063] Specifically, step S300 includes the following steps:

[0064] Step S301: Based on the system model, obtain the desired deviation value;

[0065] Step S302: Based on the desired deviation value and the initial sliding surface, obtain the target sliding surface of the adaptive sliding mode controller;

[0066] Step S303: Based on the desired deviation value, the target sliding surface and the initial parameters, calculate the target parameters of the adaptive PD controller to obtain the control parameters.

[0067] In one implementation, after obtaining the initial parameters and the initial sliding surface, as Figure 3As shown, first, based on the system model, an expected deviation value is obtained. The expected deviation value refers to the difference between the output predicted by the system model and the expected ideal output. Next, based on the initial sliding surface, a threshold range of the initial sliding surface is obtained; it is judged 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 based on the updated sliding surface, the target sliding surface of the adaptive sliding mode controller is obtained; if the expected deviation value is not within the threshold range, based on the initial sliding surface, the target sliding surface of the adaptive sliding mode controller is obtained. 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 interval, ensuring that the target system response is neither too aggressive nor too conservative, and ensuring that the control system can quickly respond and correct large deviations. After obtaining the target sliding surface of the adaptive sliding mode controller, first, based on the target sliding surface, a threshold of the target sliding surface is obtained to get a target threshold. This step is crucial for understanding the degree to which the target system may deviate from the ideal path because it directly relates 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, an adaptive damping coefficient is obtained. The adaptive damping coefficient is a key parameter used to adjust the damping effect in the control system. It determines the response characteristics of the system to input changes. To calculate this coefficient, consider the position of the expected deviation value relative to the target threshold: if the deviation is large, it means that the target system is far from the ideal sliding surface, and in this case, 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 through 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 adopted, such that the damping increases rapidly when the deviation exceeds a certain critical point, while remaining relatively smooth in other cases. Then, according to 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 modes are divided into three categories: underdamping, critical damping, and overdamping, which determine the reaction speed and stability of the target system to input signals. Specifically, according to the calculated adaptive damping coefficient D, the most suitable 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 underdamped, allowing for a quick response but possibly accompanied by oscillations; if D reaches a certain specific critical value, it indicates that the target system should be critically damped, providing the fastest response without overshoot; if D is large, it indicates that the target system should be overdamped, ensuring stability but with a slower 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 phenomena, i.e., high-frequency oscillations, which may damage the actual hardware. To alleviate this problem, an adaptive PD controller is introduced. Based on the target threshold, the desired 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 utilizes the characteristics of the adaptive PD controller that are 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 calculated and adjusted control parameters mentioned above are used as the actual input and transmitted to the motion planner. 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 previous optimizations and adjustments to ensure that the target system can not only respond quickly and precisely to changes, but also maintain a high degree of stability and robustness, thereby achieving optimal control performance. In summary, in this embodiment, the target deviation value is first obtained, and based on the target deviation value, the gain coefficient and bias coefficient of the target system are obtained, ensuring that the control mechanism can dynamically respond to internal changes and external disturbances of the target system, solving the problem of slow response 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, realizing the precise initialization of the controller parameters, avoiding the uncertainty and low efficiency brought by manual parameter adjustment, simplifying the debugging process and reducing the requirements for the operator's 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, enabling the entire control system to perform well under various working conditions, and improving the control accuracy and smoothness. The method proposed by 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 applicable to a wide range of industrial application scenarios, such as automated production, robotics, aerospace, etc.

[0068] For example Figure 4As shown in [figure number not provided], this embodiment also provides a control system based on a sliding mode controller and a PD controller. The system includes: 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 configured 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 configured to, based on the gain coefficient and the bias coefficient, obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller. The control module 30 is configured to, based on the initial parameters and the initial sliding surface, obtain control parameters and control the target system.

[0069] In one implementation, the gain coefficient and bias coefficient acquisition module 10 includes:

[0070] A historical deviation value acquisition unit configured to acquire deviation value data within a preset time period to obtain a historical deviation value;

[0071] A smoothing processing unit configured to perform smoothing processing on the target deviation value and the historical deviation value to obtain a processed deviation value, where the smoothing processing includes, but is not limited to, an extended Kalman filter algorithm;

[0072] A gain coefficient and bias coefficient acquisition unit configured to perform parameter identification on the processed deviation value to obtain the gain coefficient and bias coefficient of the target system.

[0073] In one implementation, the initial parameter and initial sliding surface acquisition module 20 includes:

[0074] A system model construction unit configured to construct a system model of the target system based on the gain coefficient and the bias coefficient;

[0075] An initial parameter and initial sliding surface acquisition unit configured to, based on the system model, obtain the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller.

[0076] In one implementation, the control module 30 includes:

[0077] An expected deviation value acquisition unit configured to acquire an expected deviation value based on the system model;

[0078] A target sliding surface acquisition unit configured to obtain the target sliding surface of the adaptive sliding mode controller based on the expected deviation value and the initial sliding surface;

[0079] A control parameter acquisition unit, configured to calculate the target parameters of the adaptive PD controller based on the desired deviation value, the target sliding surface, and the initial parameters, so as to obtain control parameters.

[0080] In one implementation, the target sliding surface acquisition unit includes:

[0081] A threshold range acquisition first sub-unit, configured to obtain the threshold range of the initial sliding surface based on the initial sliding surface;

[0082] A target sliding surface acquisition first sub-unit, configured to update the initial sliding surface if the desired deviation value is within the threshold range, so as to obtain an updated sliding surface, and obtain the target sliding surface of the adaptive sliding mode controller based on the updated sliding surface;

[0083] A target sliding surface acquisition second sub-unit, configured to obtain the target sliding surface of the adaptive sliding mode controller based on the initial sliding surface if the desired deviation value is not within the threshold range.

[0084] In one implementation, the control parameter acquisition unit includes:

[0085] A target threshold acquisition sub-unit, configured to obtain the threshold of the target sliding surface based on the target sliding surface, so as to obtain a target threshold;

[0086] A control parameter acquisition sub-unit, configured to calculate the target parameters of the adaptive PD controller based on the target threshold, the desired deviation value, and the initial parameters, so as to obtain control parameters.

[0087] In one implementation, after obtaining the target threshold, it includes:

[0088] An adaptive damping coefficient acquisition sub-unit, configured to obtain an adaptive damping coefficient based on the target threshold and the desired deviation value;

[0089] A parameter adjustment sub-unit, configured to adjust the parameters of the adaptive sliding mode controller based on the adaptive damping coefficient.

[0090] The working principles of the various modules in the control system based on the sliding mode controller and the PD controller in this embodiment are the same as those of the various steps in the above method embodiment, and will not be elaborated here.

[0091] Based on the above embodiments, the present invention further provides a terminal, and the principle block diagram of the terminal can be as Figure 5 shown. The terminal may include one or more processors 100 ( Figure 5Only 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 the one or more processors 100 execute the computer program 102, each step in the control method embodiment based on the sliding mode controller and the PD controller can be implemented. Alternatively, when the 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 herein.

[0092] In one embodiment, the so-called processor 100 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0093] In one embodiment, the memory 101 may be an internal storage unit of the electronic device, such as the 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 equipped on the electronic device, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 101 may also include both the internal storage unit and the external storage device of the electronic device. The memory 101 is used to store the computer program and other programs and data required by the terminal. The memory 101 may also be used to temporarily store the data that has been output or will be output.

[0094] Those skilled in the art can understand that Figure 5 the principle block diagram shown in the figure is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the terminal to which the solution of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0095] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, storage, operational database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can 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 data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate 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 includes: Obtaining a target deviation value, and based on the target deviation value, obtaining the gain coefficient and bias coefficient of the target system; Based on the gain coefficient and the bias coefficient, obtaining the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller; Based on the initial parameters and the initial sliding surface, obtaining control parameters and controlling the target system; The obtaining the gain coefficient and bias coefficient of the target system based on the target deviation value includes: Obtaining 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, and the smoothing process is an extended Kalman filtering algorithm; Performing parameter identification on the processed deviation value to obtain the gain coefficient and bias coefficient of the target system; The obtaining control parameters based on the initial parameters and the initial sliding surface includes: Based on the system model, obtaining an expected deviation value; Based on the initial sliding surface, obtaining the threshold range of the initial sliding surface; If the expected deviation value is within the threshold range, updating the initial sliding surface to obtain an updated sliding surface, and based on the updated sliding surface, obtaining the target sliding surface of the adaptive sliding mode controller; If the expected deviation value is not within the threshold range, based on the initial sliding surface, obtaining the target sliding surface of the adaptive sliding mode controller; Based on the target sliding surface, obtaining the threshold of the target sliding surface to obtain a target threshold; Based on the target threshold, the expected deviation value, and the initial parameters, calculating the target parameters of the adaptive PD controller to obtain control parameters; After obtaining the target threshold, it includes: Based on the target threshold and the expected deviation value, obtaining an adaptive damping coefficient; According to the adaptive damping coefficient, determining the damping response mode by comparing with a preset standard or reference value; According to the damping response mode, adjusting the parameters of the adaptive sliding mode controller.

2. The control method based on a sliding mode controller and a PD controller according to claim 1, wherein The obtaining 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 includes: Based on the gain coefficient and the bias coefficient, constructing the system model of the target system; Based on the system model, obtaining the initial parameters of the adaptive PD controller and the initial sliding surface of the adaptive sliding mode controller.

3. A control system based on a sliding mode controller and a PD controller, characterized in that, The system includes: A gain coefficient and bias coefficient acquisition module for obtaining a target deviation value and based on the target deviation value, obtaining the gain coefficient and bias coefficient of the target system; An initial parameter and initial sliding surface acquisition module for obtaining 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 for obtaining control parameters based on the initial parameters and the initial sliding surface and controlling the target system; The gain coefficient and bias coefficient acquisition module includes: A historical deviation value acquisition unit for obtaining the deviation value data within a preset time period to obtain a historical deviation value; A smoothing processing unit, configured to perform smoothing processing on the target deviation value and the historical deviation value to obtain a processed deviation value, where the smoothing processing is an extended Kalman filtering algorithm; A gain coefficient and bias coefficient acquisition unit, configured to perform parameter identification on the processed deviation value to obtain the gain coefficient and bias coefficient of the target system; The control module includes: An expected deviation value acquisition unit, configured to obtain an expected deviation value based on the system model; A target sliding surface acquisition unit, configured to obtain the 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, configured to calculate the target parameter of the adaptive PD controller based on the expected deviation value, the target sliding surface, and the initial parameter, to obtain control parameters; The target sliding surface acquisition unit includes: A threshold range acquisition first sub-unit, configured to obtain the threshold range of the initial sliding surface based on the initial sliding surface; A target sliding surface acquisition first sub-unit, configured to update the initial sliding surface if the expected deviation value is within the threshold range to obtain an updated sliding surface, and obtain the target sliding surface of the adaptive sliding mode controller based on the updated sliding surface; A target sliding surface acquisition second sub-unit, configured 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; The control parameter acquisition unit includes: A target threshold acquisition sub-unit, configured to obtain the threshold of the target sliding surface based on the target sliding surface to obtain a target threshold; A control parameter acquisition sub-unit, configured 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 control parameters; After obtaining the target threshold, it includes: An adaptive damping coefficient acquisition sub-unit, configured to obtain an adaptive damping coefficient based on the target threshold and the expected deviation value; A parameter adjustment sub-unit, configured to adjust the parameters of the adaptive sliding mode controller based on the adaptive damping coefficient; The parameter adjustment sub-unit includes: Based on the adaptive damping coefficient, determine the damping response mode by comparing with a preset standard or reference value; Adjust the parameters of the adaptive sliding mode controller according to the damping response mode.

4. 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 according to any one of claims 1-2 are implemented.

5. A computer-readable storage medium, characterized in that, A control program based on a sliding mode controller and a PD controller is stored on the computer-readable storage medium. When the control program based on the sliding mode controller and the PD controller is executed by the processor, the steps of the control method based on the sliding mode controller and the PD controller according to any one of claims 1-2 are implemented.

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