Feedback regulation method of sliding mode control system under data interconnection

Abstract

The present invention provides a feedback regulation method for a sliding mode control system under data interconnection, which relates to the technical field of sliding mode control and includes: obtaining feedback monitoring data, performing a reliability analysis and evaluation on the data source based on the quality evaluation parameters and historical data of the corresponding data source to determine the data source reliability coefficient; using a preset screening threshold to screen and set the data source reliability coefficient to obtain effective data sources; performing a feedback chain ratio setting according to the data source reliability coefficient; establishing a control update relationship between the effective data source and the sliding mode surface, and feeding back the effective data source and its feedback chain ratio of the data source to the sliding mode control system; adjusting the sliding mode surface gain to generate a control law to perform control adjustment on the sliding mode control system. The present invention solves the technical problem that the sliding mode control system in the prior art usually uses fixed sensor data input, resulting in unreliable or inaccurate data affecting the performance of the control system and making the control effect unsatisfactory.

Description

Technical Field

[0001] The present invention relates to the technical field of sliding mode control, and particularly to a feedback regulation method for a sliding mode control system under data interconnection. Background Art

[0002] Sliding Mode Control (SMC) is a non-linear control method widely used in fields where the system has strong robustness requirements for uncertainties and external disturbances. It achieves precise control of the system by designing a sliding surface such that the system state slides on this surface. The main advantages of sliding mode control lie in its strong robustness and good dynamic performance, especially when facing model uncertainties, external disturbances, and system parameter variations.

[0003] However, existing sliding mode control systems usually adopt fixed sensor data inputs, ignoring the differences in data quality and timeliness of different sensors. In many application scenarios, data provided by multiple sensors may be inconsistent due to hardware failures, environmental interference, data loss, or delays. Traditional sliding mode control methods usually lack the ability to dynamically evaluate the reliability of data sources and adaptively adjust, so the performance of the system is often affected when processing multi-sensor data. Summary of the Invention

[0004] This application provides a feedback regulation method for a sliding mode control system under data interconnection, aiming to solve the technical problem that existing sliding mode control systems usually adopt fixed sensor data inputs, ignoring the differences in data quality and timeliness of different sensors, resulting in unreliable or inaccurate data affecting the performance of the control system and making the control effect unsatisfactory.

[0005] The feedback regulation method for a sliding mode control system under data interconnection disclosed in this application includes: obtaining feedback monitoring data, performing a reliability analysis and evaluation of the data source based on the quality evaluation parameters and historical data of the data source corresponding to the feedback monitoring data to determine the data source reliability coefficient; using a preset screening threshold to screen and set the data source reliability coefficient to obtain valid data sources; performing a feedback link ratio setting according to the data source reliability coefficient of the valid data sources; establishing a control update relationship between the valid data sources and the sliding surface, and feeding back the valid data sources and their feedback link ratios to the sliding mode control system; adjusting the sliding surface gain according to the feedback link ratio to generate a control law to perform control adjustment on the sliding mode control system.

[0006] One or more technical solutions provided in this application have at least the following beneficial effects:

[0007] By obtaining feedback monitoring data and performing reliability analysis and evaluation on the data source, the reliability of the data source can be evaluated based on the quality evaluation parameters and historical data of the data source. This process ensures that the data is screened based on its reliability when used, thereby improving the accuracy of the data source. This method can effectively exclude unreliable or low-quality data sources and avoid their negative impact on system control; by using the preset screening threshold to screen the reliability coefficient of the data source, an effective data source is obtained. This screening process ensures that only reliable data sources are selected into the control system, which helps to reduce the impact of noise data on the control system. The dynamic screening mechanism enables the system to select the best input data in real time according to changes in the data source, thereby enhancing the flexibility and adaptability of the system; the feedback loop is set based on the reliability coefficient of the effective data source. The adaptive adjustment of the feedback loop can dynamically change the influence of each data source in the control system according to its quality and reliability. , reliable data sources will have a greater impact on the control system, while the impact of unreliable data sources is effectively reduced. This process ensures that the control system can maintain stable and precise control when the quality of the data source fluctuates; by establishing a control update relationship between the effective data source and the sliding surface, and returning the feedback loop to the sliding mode control system, it is ensured that the system can adjust the sliding surface according to the real-time effective data. This control update mechanism allows the system to dynamically adjust according to the actual data quality, optimize the controller response and stability, and improve the overall performance of the control system; adjust the sliding surface gain according to the feedback loop ratio to generate the control law for control adjustment. This step dynamically adjusts the sliding surface gain so that the control system can maintain a high robustness when facing data sources of different quality, enhances the system's adaptability to changes in data quality, and ensures that the system can make accurate control decisions even in the event of sensor failure or data fluctuations.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flow chart of a feedback adjustment method for a sliding mode control system under data interconnection is provided for an embodiment of the present application.

[0010] Figure 2 A schematic diagram of a data compensation process in a feedback adjustment method of a sliding mode control system under data interconnection is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0011] By providing a feedback regulation method for a sliding mode control system under data interconnection, the embodiments of the present application solve the technical problem that the sliding mode control systems in the prior art usually adopt fixed sensor data input, ignoring the differences in data quality and timeliness of different sensors, resulting in unreliable or inaccurate data affecting the performance of the control system and making the control effect unsatisfactory.

[0012] After introducing the basic principle of the present application, the various non-limiting embodiments of the present application will be specifically introduced below with reference to the accompanying drawings of the specification.

[0013] As Figure 1 shown, the embodiments of the present application provide a feedback regulation method for a sliding mode control system under data interconnection, and the method includes:

[0014] Obtain feedback monitoring data, and perform a reliability analysis and evaluation on the data source based on the quality evaluation parameters and historical data of the data source corresponding to the feedback monitoring data to determine the data source reliability coefficient.

[0015] The feedback monitoring data is data collected by sensors or measuring devices in a sliding mode control system. These data are used to monitor the state and performance of the system in real time, including various types such as the temperature, pressure, current, and speed of the system, specifically depending on the application field of the control system.

[0016] The quality evaluation parameters are indicators for evaluating different quality characteristics of the data source, including data accuracy, integrity, timeliness, stability, consistency, etc. These evaluation parameters can reflect the reliability and accuracy of the data source and are the basis for evaluating the data source. Perform a reliability analysis and evaluation on the data source based on the quality evaluation parameters. Specifically, through the feedback monitoring data, the corresponding quality evaluation parameters are extracted. For example, for sensor data, its accuracy, error range, etc. can be evaluated. Analyze and calculate these quality evaluation parameters. Usually, a certain weight is assigned to each parameter to represent its importance in the overall evaluation. A weighted algorithm is used to comprehensively calculate multiple evaluation parameters to obtain the comprehensive quality score of each data source, and this score is the reliability evaluation of the data source.

[0017] The historical data refers to the data collected by the system over a period of time in the past. These data can provide clues about the stability of the data source. Especially during long-term use, the data source may show different performance fluctuations. Perform a reliability analysis based on the historical data. Specifically, analyze the historical data to evaluate the long-term reliability of the data source. The methods include error analysis, time series analysis, stability evaluation, etc. Integrate the time series characteristics of these historical data to evaluate the stability and reliability of the data source and obtain a stability score, indicating the reliability of the data source during long-term operation.

[0018] Based on the comprehensive analysis results of quality evaluation parameters and historical data, a reliability coefficient for each data source is obtained. This coefficient reflects the credibility of the data source and is used to measure the contribution of the data source to the control system. The reliability coefficient is a value between 0 and 1, where 1 indicates that the data source is very reliable and 0 indicates that the data source is completely unreliable. For different data sources, different reliability coefficients can be assigned according to their quality evaluation and historical stability results.

[0019] Use a preset screening threshold to screen and set the reliability coefficient of the data source to obtain valid data sources.

[0020] Use a preset screening threshold to screen the reliability coefficient of the data source to determine which data sources are valid and which should be excluded. Specifically, the preset screening threshold is preset according to the specific requirements and design standards of the system. For example, if the threshold is set to 0.5, it means that only data sources with a reliability coefficient greater than or equal to 0.5 are considered credible. Evaluate the reliability coefficients of all data sources and compare them with the preset screening threshold. Those data sources with reliability coefficients higher than the threshold are selected as valid data sources for further control and analysis.

[0021] Perform a feedback loop ratio setting according to the reliability coefficient of the valid data source.

[0022] Use the reliability coefficient of the valid data source to set the feedback loop ratio of each valid data source. The feedback loop ratio refers to adjusting the weight of the control input according to the data validity. For example, if the data provided by a certain sensor is very accurate and timely, then the feedback impact of this data source on the sliding mode controller may be enhanced; conversely, if the validity of the data source is low, the impact of the feedback on the control system may be reduced. The feedback loop ratio of each data source is used to adjust the impact of the data source on the controller output. The larger the feedback loop ratio of the data source, the greater its impact on the controller's decision-making.

[0023] Establish the control update relationship between the valid data source and the sliding mode surface, and feedback the valid data source and its feedback loop ratio to the sliding mode control system.

[0024] Integrate the valid data source and its calculated feedback loop ratio into the control logic of the sliding mode control system to ensure that changes in the data source can affect the control strategy in real time. Specifically, first determine how to integrate the information (such as sensor readings) of each valid data source and its feedback loop ratio into the calculation model of the control system to ensure that the data can be correctly reflected in the control output. The sliding mode surface is a mathematical expression used in the control system design to describe the error between the system state and the desired state. In this step, adjust the definition of the sliding mode surface according to the feedback loop ratio of the valid data source, such as adjusting the weight of the error term, to reflect the importance and reliability of different data sources.

[0025] Adjust the sliding mode surface gain according to the feedback ratio, and generate a control law to control and adjust the sliding mode control system.

[0026] The sliding mode surface gain determines the response speed and intensity of the system to deviations. By adjusting these gain parameters, usually proportional, integral, and derivative gains, the control response can be optimized according to the reliability of the effective data source. For example, if a data source is very reliable and its corresponding feedback ratio is high, the related control gain can also be set higher to respond faster to changes in this data source. The control law is designed based on the adjusted sliding mode surface, which defines how to calculate the control input that needs to be applied to the system from the current state of the sliding mode surface, including but not limited to control strategies for linear and non-linear systems. By establishing the control update relationship between the effective data source and the sliding mode surface, and adjusting the sliding mode surface gain based on the feedback ratio, it is ensured that the sliding mode control system can dynamically adjust its behavior according to the most reliable and relevant data. Such a strategy helps to improve the accuracy and adaptability of the system and can maintain performance and stability under changing environments and conditions.

[0027] Furthermore, as Figure 2 shown, after obtaining the effective data source, it further includes:

[0028] Judge whether the effective data source meets the control requirements; when it does not meet the control requirements, perform data compensation according to the effective data source, add the compensation data to the effective data source, and set the reliability coefficient of the compensation data source according to the compensation settings.

[0029] The control requirement is the minimum quantity required for the effective data source designed by the system. Check whether the quantity of the effective data source reaches the minimum quantity required by the system design. If the data source is insufficient to cover all control variables of the system or cannot provide the required data quality, further processing is required.

[0030] When it is judged that the effective data source fails to meet the control requirements, a data compensation mechanism needs to be implemented. Specifically, first, clarify the types of data to be compensated, such as temperature, pressure, etc., as well as the quantity and quality to be compensated. Generate compensation data by adding additional sensors or using software algorithms. For example, if the data of a certain sensor is unstable, the data of other sensors can be introduced for averaging or weighted processing to enhance the stability of the data. According to the generation method and accuracy of the compensation data, set appropriate reliability coefficients for these data. For example, if the compensation data is obtained through data fusion of multiple sensors, its reliability coefficient may be higher than that of data from a single sensor. Integrate the compensation data and its reliability coefficient into the control system to ensure that these data can be correctly used by the system and adjust the corresponding control strategy.

[0031] Furthermore, it further includes:

[0032] Set multi - level screening thresholds, where the multi - level screening thresholds are multi - level confidence intervals for screening data sources; use the multi - level confidence intervals to perform multi - level matching and identification on the reliability coefficients of the data sources to obtain the effective levels of the data sources; configure priority factors according to the effective levels of the data sources, where the priority factors are used to correct the feedback ratio, and the higher the effective level, the larger the corresponding priority factor.

[0033] According to the reliability requirements of different data sources, set multi - level screening thresholds. These thresholds are divided into multiple levels and are used to construct multi - level confidence intervals. For example, three levels of thresholds can be set, such as high level, medium level, and low level. The thresholds corresponding to each level reflect the reliability of the data sources, ensuring that the screened data sources can meet different control requirements. These level thresholds form multiple confidence intervals, and the reliability coefficient of each data source will be mapped to the corresponding interval. In this way, different control strategies can be adopted according to the reliability of different data sources.

[0034] For each valid data source, determine the confidence interval it belongs to according to its reliability coefficient. For example, if the reliability coefficient of a data source is 0.7 and the set confidence interval is from 0.5 to 0.8, then this data source is classified as medium reliability. Through the matching process, the effective level of each data source is identified. The higher the effective level of the data source, the stronger its reliability and the more valuable information it can provide for the control system.

[0035] According to the effective level of the data source, configure a priority factor for each data source to correct the feedback ratio of the data source to the sliding - mode control system. The priority factor is used to represent the importance of data sources at different levels. Generally speaking, the higher the effective level of the data source, the larger the configured priority factor. This means that high - level data sources will have a greater impact on the feedback ratio, while low - level data sources have a smaller impact. When the priority factors are determined, apply these factors to the calculation of the feedback ratio. The priority factors will correct the feedback ratio, so that the reliability of the data source directly affects its contribution to the control system. Specifically, the feedback ratio with a higher priority factor will have a stronger impact on the control system, while the feedback ratio with a lower priority factor has a smaller impact. This process ensures that the sliding - mode control system can adjust the feedback ratio according to the quality of the data source, can make reasonable responses in the face of changes in the quality of different data sources, and thus optimize the control effect.

[0036] Furthermore, according to the reliability coefficient of the valid data source, perform feedback ratio setting, including:

[0037] Through the feedback ratio calculation formula: , calculate the feedback ratio, where, is the feedback ratio of the i-th data source, is the reliability coefficient of the i-th data source, is the set of valid data sources; the feedback ratio is corrected using the priority factor, and the corrected expression of the feedback ratio calculation formula is: , where k is the priority factor, is the priority factor of the i-th data source.

[0038] The feedback ratio calculation formula is as follows: , where represents the feedback ratio of the i-th data source at time t. The feedback ratio is an index used to measure the contribution weight of each data source to the sliding mode control system; is the reliability coefficient of the i-th data source at time t, indicating the reliability degree of the data source. The higher the reliability coefficient, the more reliable the data source, and vice versa; is the set of valid data sources at time t, meaning all valid data sources with sufficient reliability at this time; is the sum of the reliability coefficients of all valid data sources at time t, reflecting the total reliability of all valid data sources.

[0039] Overall, describes the proportion of the i-th data source among all valid data sources. The higher the reliability coefficient of the data source, the corresponding feedback ratio is larger, indicating that the data source makes a greater contribution to the control system, and vice versa. This formula helps to determine the influence degree of each data source on the feedback of the sliding mode control system.

[0040] The feedback ratio is corrected using the priority factor, and the expression is: , where is the priority factor of the i-th data source, which represents the relative priority of the data source among all data sources. The larger the priority factor, the higher the importance of the data source in the feedback calculation. This formula shows that on the basis of the original calculation of the feedback ratio, the priority factor is added, so as to correct the influence of the data source on the sliding mode control system according to the priority of the data source. Specifically, by introducing the priority factor, the proportion of the data source in the feedback ratio is adjusted. If a certain data source has a high priority factor , its feedback ratio will increase accordingly, and vice versa. The priority factor is usually set according to the importance or reliability of the data source. This method enables the system to flexibly adjust the influence of different data sources, ensuring that the most important and reliable data sources have a greater feedback impact on the control system, and improving the accuracy and stability of the overall system. In short, this correction process further weights and adjusts the feedback ratio of each data source through the priority factor to ensure that the data sources crucial to the control system obtain higher weights, further enhancing the responsiveness and robustness of the system.

[0041] Furthermore, based on the quality evaluation parameters and historical data of the data source corresponding to the feedback monitoring data, a reliability analysis and evaluation of the data source is carried out to determine the data source reliability coefficient, including:

[0042] According to the quality evaluation parameters of the data source, each parameter evaluation is carried out respectively to obtain the evaluation values of multi-dimensional quality evaluation parameters; obtain the influence of each dimension of quality evaluation parameters on the reliability of the data source, and set the real-time evaluation weight; based on the real-time evaluation weight, perform a weighted calculation on the evaluation values of the multi-dimensional quality evaluation parameters to obtain the quality evaluation result; conduct a reliable stability evaluation of the data source according to the historical data to obtain the time-series stability evaluation result; conduct a comprehensive evaluation according to the quality evaluation result and the time-series stability evaluation result to determine the data source reliability coefficient.

[0043] To comprehensively evaluate the quality of the data source, it is first necessary to determine quality evaluation parameters in multiple dimensions. These parameters include but are not limited to data accuracy, timeliness, stability, consistency, integrity, etc. Independently evaluate the quality parameters of each dimension, and score according to the performance of each parameter. For example, if a data source is very excellent in terms of accuracy, a relatively high score may be assigned to it; if it performs poorly in terms of timeliness, the score may be relatively low.

[0044] Analyze the contribution degree of different quality parameters to the reliability of the data source. For example, accuracy may have a greater impact on reliability, while timeliness may not be as important as accuracy in some applications. According to the importance of each quality parameter, set the real-time evaluation weight of each parameter. For example, if accuracy is crucial to the control system, a relatively high weight can be assigned to it, while the weight of timeliness may be relatively low. The real-time weight reflects the contribution ratio of each evaluation parameter to the reliability of the data source in the actual control process.

[0045] According to the set real-time weights, the evaluation values of each quality parameter are weighted and summed. Through weighted calculation, the evaluation values of each dimension are synthesized into a final quality evaluation result. The calculated result after weighting is the quality evaluation result of the data source, which reflects the comprehensive quality level of the data source in different dimensions. The quality evaluation result is a value between 0 and 1, and the larger the value, the more reliable the data source.

[0046] Evaluate the stability of each data source based on historical data, with particular attention to temporal stability. Temporal stability involves the stability, change trend, and volatility of the data source over a long period. Specifically, perform temporal data analysis, that is, based on historical data, analyze the changes of the data source in the time series. For example, for lidar data, evaluate the historical stability and deviation of lidar data based on past positioning errors; conduct stability evaluation. For example, in an autonomous driving system, the historical positioning deviation of lidar can be used to evaluate the temporal stability of lidar. If the positioning error of lidar changes little over time, it indicates good temporal stability. If the error is large and unstable, it indicates poor stability of the data source; evaluate temporal volatility. For an industrial automation system, the long-term drift of monitoring sensors is also part of temporal stability. If a temperature sensor operates stably for a long time and its data volatility is small, then its temporal stability is also good. Finally, through temporal stability analysis, generate a temporal stability evaluation result, that is, the stability score of the data source during long-term operation.

[0047] Combine the quality evaluation result and the temporal stability evaluation result, and use methods such as weighted average to calculate the final reliability coefficient. The finally obtained reliability coefficient is between 0 and 1, and the larger the value, the higher the reliability of the data source. Data sources with higher reliability coefficients will have a greater impact on the feedback loop ratio of the sliding mode control system, ensuring that the system can make more accurate and stable control decisions.

[0048] Furthermore, conduct a reliable stability evaluation of the data source based on the historical data to obtain a temporal stability evaluation result, including:

[0049] Calculate the error value of the data source based on the historical data, and perform quality stability time division according to the error value, where the divided time zone has a stable evaluation value identifier; align the acquisition time of the feedback monitoring data with the time of the divided time zone, identify the stable evaluation value of the feedback monitoring data, and obtain the temporal stability evaluation result.

[0050] For each data source, calculate the error value of its historical data. The error value refers to the deviation between the actual observed value and the predicted value or expected value. For example, in an autonomous driving system, the error value of GPS positioning data can be obtained by calculating the difference between the actual GPS position and the expected position. Based on these error values, the system analyzes the stability of the data. If the error values fluctuate greatly, it indicates that the stability of the data source is poor; if the error values are small and the fluctuations are stable, it indicates that the data source is relatively reliable.

[0051] According to the changes in the error values, divide the historical data into different time zones. Each time zone represents the stability level of the data source during that period. For example, data with small and stable error values can be divided into one time zone, while data with large and fluctuating error values can be divided into another time zone. Each time zone is marked with a stability evaluation value, which represents the stability of the data source within that time zone. For example, a time zone with high stability may receive an evaluation with an identification value of "1", indicating that the data within that time zone is very stable; while an unstable time zone may receive an evaluation with an identification value of "0".

[0052] Feedback monitoring data is usually collected at different time points. Compare the timestamp of the real-time collected feedback data with the previously divided time zones to determine which time zone the currently collected feedback data belongs to. For example, if the real-time feedback data of a certain data source is collected within time zone 2 of the historical data, the data will be compared with the data stability of time zone 2.

[0053] When the collection times are aligned, evaluate the stability of the current feedback data according to the stability evaluation value of that time zone. If the feedback data is in a time zone with high stability, it indicates that the data source performs stably within that time zone; if the feedback data is in an unstable time zone, it will receive a lower evaluation value.

[0054] Finally, through the alignment and analysis of the feedback data and the time zone stability evaluation values, obtain the time-series stability evaluation result of the feedback data. This result reflects the stability level of the feedback data over time. For example, if the data source is very stable during a certain period in history and the real-time data is aligned with that period, the time-series stability evaluation result is high, indicating that the data source is reliable; otherwise, the stability is low.

[0055] Furthermore, it also includes:

[0056] When the sliding mode control system includes multiple sub - sliding mode controllers, obtain the cooperative control relationship of the multiple sub - sliding mode controllers; analyze the inter - connection data consistency requirements among the sub - sliding mode controllers according to the cooperative control relationship, conduct feedback delay constraint analysis based on the parsed consistency requirements to obtain the inter - connection data delay compensation constraint; and perform feedback time control on the inter - connection data of each sub - sliding mode controller according to the inter - connection data delay compensation constraint.

[0057] Define the cooperative control relationship among the individual sub - sliding mode controllers. This involves multiple sub - controllers jointly achieving a complex task. For example, multiple controllers are respectively responsible for different control variables such as position, speed, temperature, etc., but they need to coordinate together to ensure the consistency and performance of the entire system. The relationship between different sub - controllers may be independent of each other or there may be data dependencies. For example, the output of a certain sub - controller may become the input of another sub - controller. Clearly defining these dependencies helps with subsequent delay compensation and data consistency analysis.

[0058] The data consistency requirements among the sub - sliding mode controllers include state consistency, control consistency, and time consistency in data exchange between controllers. For example, different sub - controllers may need to obtain the same data at a specific moment, or there may be certain timing requirements among their data. Since the data of multiple sub - controllers need to be exchanged through a communication network or bus, delays (such as data transmission delays) may lead to data inconsistency. By conducting delay constraint analysis, evaluate the impact of this delay on the performance of the control system. For example, the delay in data transmission may cause a lag in controller response, affecting the stability and response speed of the system. According to the delay analysis, calculate an appropriate delay compensation strategy to ensure data consistency. The compensation strategy includes technical means such as adjusting the update frequency of the controller, adding a buffering mechanism, or using timestamp synchronization to reduce data inconsistency caused by delays.

[0059] Based on the obtained delay compensation constraint, adjust the time of data feedback among the individual sub - sliding mode controllers. Specifically, by setting an appropriate feedback time window, ensure that each sub - controller can receive the updated data from other sub - controllers in a timely manner and make reasonable adjustments to its feedback. If the data of some sub - controllers is delayed significantly, the time of its output feedback can be delayed, or interpolation algorithms and other techniques can be used to compensate for the delay. Through reasonable time control, optimize the coordination of each sub - controller to ensure that the output of each sub - controller is fed back to other controllers at the correct time point, avoiding mismatches or the use of outdated data caused by delays. This process effectively improves the coordination among multiple sub - controllers and ensures that the system can maintain good stability and dynamic response when multiple sub - controllers work together.

[0060] Furthermore, based on the collaborative control relationship, analyze the interconnection data consistency requirements among the sub-sliding mode controllers, including:

[0061] Analyze the requirements from the perspective of state consistency to determine the alignment requirements of state variables among the sub-sliding mode controllers; analyze the requirements from the perspective of control consistency to determine the coupling requirements of inputs and outputs among the sub-sliding mode controllers; analyze the requirements from the perspective of time consistency to determine the exchange time consistency requirements of the interconnection data.

[0062] Analyze and determine the state consistency requirements among the sub-sliding mode controllers to ensure that their state variables can be aligned during the control process. Specifically, define the state variables, that is, each sub-controller is responsible for controlling specific variables, such as speed, position, temperature, etc. The state variables reflect the current state of the control system and are the core inputs of the control algorithm. When multiple sub-controllers work together, it is necessary to ensure that the state variables of each controller can be synchronized and coordinated. For example, the output of sub-controller A may be the input of sub-controller B. If the state variables of A and B are not synchronized, it may lead to the control decision of B being based on inaccurate state settings, thus affecting the performance of the entire system. At this time, it is necessary to ensure that the state variables of all relevant sub-controllers have consistent values at the same moment. For example, if there is a time lag between the state variable of one sub-controller and the input variable of another sub-controller, they must be aligned through timing synchronization or time delay compensation.

[0063] Analyze and determine the control consistency requirements among the sub-sliding mode controllers, especially the input and output coupling requirements between them. Specifically, in a multi-sub-controller system, the output of one sub-controller often serves as the input of other sub-controllers, which requires the system to clarify the coupling relationship between the inputs and outputs of each sub-controller. For example, the output of sub-controller A may be the input of sub-controller B. To ensure the correctness of the control, B needs to obtain the output of A. When there is data coupling between the controllers, the output data of A needs to be transmitted to B in a timely manner and must be accurate. If the data of A obtained by B is delayed or in error, it may lead to inaccurate control decisions, thus affecting the stability of the system. Based on the requirements of input and output coupling, it is necessary to ensure that the input and output data between the controllers are updated in a timely manner and remain accurate, especially in real-time control, where data delay and loss will increase the error of the control system.

[0064] Analyze and determine the data exchange timing requirements between each sub - controller to ensure the temporal consistency of data. Specifically, when data needs to be exchanged between multiple sub - controllers, it is necessary to ensure that the time points of data exchange are consistent. If the data exchange between controllers is not synchronized, it may lead to lag or distortion in data usage. By parsing, define the specific time points of data exchange. For example, sub - controller A may send data to sub - controller B at a certain moment, and sub - controller B must make control decisions as soon as possible after receiving the data. Any time delay or timing inconsistency may affect the response speed of the controller and even lead to instability of the control system. To meet the temporal consistency requirements, synchronization mechanisms such as timestamps, data caching, or coordinated control strategies can be used. These methods ensure that the data exchange between different sub - controllers follows a unified time framework, thus avoiding control errors caused by time deviation.

[0065] Generally speaking, through the above - mentioned process, the collaborative requirements between multi - sub - sliding - mode controllers are analyzed from three aspects: state consistency, control consistency, and temporal consistency. Among them, state consistency ensures that the state variables of each sub - controller are aligned in time, avoiding incorrect control caused by data inconsistency; control consistency analyzes the coupling relationship between input and output to ensure that the input data of the sub - controller is timely and accurate; temporal consistency ensures the synchronization of data exchange between each controller, thus avoiding control problems caused by time delay. The purpose of these steps is to ensure that each sub - controller in the sliding - mode control system can work in coordination and maintain consistency in data exchange and feedback control, thereby enhancing the robustness and stability of the entire system.

[0066] In summary, the feedback regulation method of the sliding - mode control system under data interconnection provided by the embodiments of this application has the following technical effects:

[0067] By obtaining feedback monitoring data and performing reliability analysis and evaluation on data sources, the reliability of data sources can be evaluated based on the quality evaluation parameters and historical data of the data sources. This process ensures that data is screened based on its reliability when used, thereby improving the accuracy of data sources. This method can effectively exclude unreliable or low-quality data sources and avoid their negative impact on system control. By using a preset screening threshold to screen the reliability coefficient of data sources, effective data sources are obtained. This screening process ensures that only reliable data sources are selected into the control system, which helps reduce the impact of noise data on the control system. The dynamic screening mechanism enables the system to select the best input data in real time according to changes in data sources, thereby enhancing the flexibility and adaptability of the system. Based on the reliability coefficient of effective data sources, feedback loop ratios are set. The adaptive adjustment of feedback loop ratios can dynamically change the influence of each data source in the control system according to its quality and reliability. Reliable data sources will have a greater impact on the control system, while the impact of unreliable data sources is effectively reduced. This process ensures that the control system can maintain stable and precise control even when the quality of data sources fluctuates. By establishing the control update relationship between effective data sources and the sliding mode surface and returning the feedback loop ratio to the sliding mode control system, it is ensured that the system can adjust the sliding mode surface according to real-time effective data. This control update mechanism enables the system to make dynamic adjustments according to the actual data quality, optimize the controller response and stability, and improve the overall performance of the control system. Adjust the sliding mode surface gain according to the feedback loop ratio to generate a control law for control adjustment. This step dynamically adjusts the sliding mode surface gain, enabling the control system to maintain high robustness when facing data sources of different qualities, enhancing the adaptability of the system to changes in data quality, and ensuring that the system can still make accurate control decisions even in the case of sensor failures or data fluctuations.

[0068] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A feedback regulation method for a sliding mode control system under data interconnection, characterized in that, The method includes: Obtaining feedback monitoring data, performing a reliability analysis and evaluation on the data source based on the quality evaluation parameters and historical data of the data source corresponding to the feedback monitoring data, and determining the data source reliability coefficient; Using a preset screening threshold to screen and set the data source reliability coefficient to obtain valid data sources; Performing a feedback month-on-month setting according to the data source reliability coefficient of the valid data source; Establishing a control update relationship between the valid data source and the sliding mode surface, and feeding back the valid data source and its feedback month-on-month ratio of the data source to the sliding mode control system; Adjusting the sliding mode surface gain according to the feedback month-on-month ratio to generate a control law to control and adjust the sliding mode control system; It also includes: Setting a multi-level screening threshold, where the multi-level screening threshold is a multi-level credible interval for screening data sources; Performing multi-level matching and identification on the data source reliability coefficient using the multi-level credible interval to obtain the effective level of the data source; Configuring a priority factor according to the effective level of the data source, where the priority factor is used to correct the feedback month-on-month ratio, and the higher the effective level, the larger the corresponding priority factor; Performing a feedback month-on-month setting according to the data source reliability coefficient of the valid data source, including: By the feedback month-on-month calculation formula: Calculate the feedback month-on-month, where R i (t) is the feedback month-on-month of the i-th data source, a i (t) is the reliability coefficient of the i-th data source, and D(t) is the set of valid data sources; The feedback ratio is corrected using a priority factor, and the correction expression of the feedback ratio calculation formula is: k is the priority factor, k i is the priority factor of the i-th data source.

2. The feedback regulation method of the sliding mode control system under data interconnection according to claim 1, characterized in that, After obtaining the valid data source, it also includes: Judging whether the valid data source meets the control requirements; When the control requirements are not met, performing data compensation according to the valid data source, adding the compensation data to the valid data source, and setting the reliability coefficient of the compensated data source according to the compensation setting.

3. The feedback regulation method of the sliding mode control system under data interconnection according to claim 1, characterized in that Performing a reliability analysis and evaluation on the data source based on the quality evaluation parameters and historical data of the data source corresponding to the feedback monitoring data, and determining the data source reliability coefficient, including: According to the quality evaluation parameters of the data source, performing respective parameter evaluations to obtain the evaluation values of multi-dimensional quality evaluation parameters; Obtaining the influence of each dimension of quality evaluation parameters on the reliability of the data source, and setting a real-time evaluation weight; Performing a weighted calculation on the evaluation values of the multi-dimensional quality evaluation parameters based on the real-time evaluation weight to obtain a quality evaluation result; Performing a reliable stability evaluation on the data source according to the historical data to obtain a time-series stability evaluation result; Performing a comprehensive evaluation according to the quality evaluation result and the time-series stability evaluation result to determine the data source reliability coefficient.

4. The feedback regulation method of the sliding mode control system under data interconnection according to claim 3, characterized in that, Performing a reliable stability evaluation on the data source according to the historical data to obtain a time-series stability evaluation result, including: Calculating the error value of the data source according to the historical data, and performing quality stability time-zone segmentation according to the error value, where the segmented time zones have stable evaluation value identifiers; Aligning the acquisition time of the feedback monitoring data with the time of the segmented time zones to identify the stable evaluation value of the feedback monitoring data to obtain the time-series stability evaluation result.

5. The feedback regulation method of the sliding mode control system under data interconnection according to claim 1, characterized in that, It also includes: When the sliding mode control system includes multiple sub-sliding mode controllers, obtaining the cooperative control relationship of the multiple sub-sliding mode controllers; Performing an analysis on the interconnection data consistency requirements between the sub-sliding mode controllers according to the cooperative control relationship, and performing a feedback delay constraint analysis based on the obtained consistency requirements to obtain the interconnection data delay compensation constraint; According to the interconnection data delay compensation constraint, feedback time control is performed on the interconnection data of each sub-sliding mode controller.

6. The feedback adjustment method of the sliding mode control system under data interconnection according to claim 5, characterized in that, According to the collaborative control relationship, the interconnection data consistency requirements between each sub-sliding mode controller are analyzed, including: Analyzing the requirements from the perspective of state consistency to determine the alignment requirements of state variables between sub-sliding mode controllers; Analyzing the requirements from the perspective of control consistency to determine the coupling requirements of inputs and outputs between sub-sliding mode controllers; Analyzing the requirements from the perspective of time consistency to determine the exchange time consistency requirements of interconnection data.

Images