Research On Quantized Feedback Sliding Mode Control Based On Event Triggerin

The advances in control theory and automation technology have penetrated into various fields of modern industry.Signal transmission plays a crucial role in industrial production,and the quality of control signal transmission greatly affects the overall performance of control systems.With the advancement of modern control technology,the emergence of networked control systems has brought the quantization problem of signals to the forefront.The control strategies commonly used in networked control systems include event-triggered control and quantized control.Event-triggered control improves the efficiency and stability of control systems,while quantized control reduces communication bandwidth and energy consumption by quantizing control signals into discrete values and minimizing the amount of data and transmission delay required for transmission.Therefore,designing a reasonable and effective event-triggering mechanism and quantization strategy is of great importance in networked control systems.However,in practical applications,there are often uncertainties such as external disturbances,and sliding mode control is a robust control strategy that can effectively resist the effects of external disturbances and system uncertainties.Therefore,the co-design of event-triggering mechanisms,quantization strategies,and sliding mode control needs to be addressed urgently.This thesis focuses on the above issues and mainly includes:(1)The quantized feedback sliding mode control of a class of multi-input linear systems is studied using an event-triggered approach.Firstly,a state quantization-based event-triggering condition is designed,and a discrete update strategy combining quantizer sensitivity parameters is used to sample and quantize the system state for feedback control.The relationship between the event triggering threshold parameter,the quantizer sensitivity parameter update condition,and the quantizer measurement saturation parameter is studied.Secondly,a quantized feedback sliding mode control law is constructed to ensure the reachability of the expected sliding surface.The lower limit of the inter-execution time intervals during the open-loop zoom-out and closedloop zoom-in processes are analyzed to ensure that Zeno phenomenon does not occur during system operation.Finally,the validity of the theoretical results is verified through experiments.(2)Based on the research in(1),considering the quantizer parameter mismatch problem in the quantization process,a quantized feedback sliding mode control method is designed for linear uncertain systems using event-triggered technology.Firstly,the time-varying quantizer sensitivity change rate is considered in the dynamic quantization strategy,a new discrete online adjustment strategy is proposed,and a time-varying proportion model of the quantization parameters on the encoder and decoder side is established.Secondly,based on the eventtriggering mechanism designed in(1),it is proven that the proposed sliding mode control law can effectively eliminate the influence of quantizer parameter mismatch and achieve global robust stabilization of uncertain linear systems.Finally,the effectiveness and superiority of the theoretical results were verified through experiments.(3)Based on the research in(1)and(2),this thesis explores the quantizer saturation problem in linear systems using the event-triggered mechanism proposed in(1)and the dynamic quantization strategy proposed in(2).It also considers the effects of external disturbances and quantizer sensitivity parameter mismatch,and studies a design method for a sliding mode controller based on an extended observer with a joint event-triggering technique and quantization strategy.Firstly,an extended observer is used to estimate unknown disturbances,and the convergence condition of the observation error is analyzed.Secondly,a quantized feedback sliding mode controller is designed based on the dynamic quantization strategy and event-triggered mechanism,and the convergence of the system trajectory is proved.Finally,the effectiveness of the theoretical results is verified through numerical examples.