Sampled-Data Control For Nonlinear Systems Based On T-S Fuzzy Models

The T-S fuzzy model is a typical method for studying nonlinear systems,which can effectively solve the problem of complex nonlinear control systems in real life.With the existence of nonlinear systems in practical engineering systems,the classical control theory has been limited,and further research is needed.In this paper,based on T-S fuzzy model and fuzzy neural network model,the sampling problem of control system is analyzed,and the sampling control system is studied by input time-delay method.By constructing Lyapunov-Krasovskii functionals,combined with linear matrix inequalities,free weight matrices and convex optimization methods,the conservative criteria and the design criteria of the controller are given respectively.The specific work is as follows:1)The sampling control of nonlinear systems based on T-S fuzzy model is studied.Considering the problem of signal delay,interference and uncertainty in network signal transmission,design a T-S fuzzy system model with uncertainty.Make full use of the information of time-varying sampling time and the function characteristics of nonlinear systems,construct segmented Lyapunov-Krasovskii functionals,combine linear matrix inequalities(LMIs)to analyze the stability of the system,and design the sampling controller.The effectiveness and feasibility of the method used in this paper are verified by simulation.2)The problem of sampling control of fuzzy neural network model is studied.Based on the structural characteristics of the neural network model,the fuzzy rules are used to approximate the nonlinear principle.The fuzzy neural network error system model with random uncertainty is obtained through analysis.Construct a suitable Lyapunov-Krasovskii functional,and then use the Schur complement theorem and Free-Matrix-Based inequality to rectify the stability of the sampling control system.The maximum allowable sampling period is obtained by LMIs method.Finally,the effectiveness and superiority of the method are proved by simulation.Figure 8;Table 3;Reference 50...