Research On Fault Diagnosis Of Networked T-S Fuzzy System

In recent years,communication and computer technologies have developed rapidly.The research of control systems has also developed rapidly in the direction of complexity and networking.These changes have led to the emergence of networked control systems.However,due to the limitations of network bandwidth and environment,there are many challenges in networked control systems.Most existing results focuse on linear systems,but the actual systems are generally nonlinear,and the T-S fuzzy model can transform a nonlinear system into a collection of linear systems.Therefore,the problem of fault diagnosis for networked T-S fuzzy systes is worthy of in-depth study.(1)For the problem of fault estimation of T-S fuzzy systems with quantification effects and system uncertainties,the state is estimated by constructing a new sliding mode observer,and through the design of the sliding mode observer switching items to offset the impact of quantization and uncertainty.By solving a linear matrix inequality and setting the quantization density of the dynamic quantizer to make the system being exponential stability,the system error can reach the sliding mode surface within a finite time,and then the output error equivalent injection method is used to estimate the fault.Finally,the effectiveness of the proposed method is verified by using numerical simulation.(2)For the fault estimation problem of T-S fuzzy system with integral measurement and time delay,both actuator faults and sensor faults are estimated simultaneously.For sensor faults,combining sensor faults and states into an augmented vector,and an H? observer is constructed to estimate the state and actuator faults,sensor faults can be obtained by estimating the state.Then,by setting a Lyapunov function and solving a linear matrix inequality,the parameter matrices of the H? observer are obtained,and the system is H? stable.Finally,through numerical simulation,the state and fault information are analyzed to verify the effectiveness of the proposed method.