Adaptive Fuzzy Control For Nonlinear Pure-feedback Systems

Adaptive fuzzy control is an effective method to solve the problem of modeling and control of complex nonlinear systems. As fuzzy systems have universal approximation characteristics, adaptive fuzzy control of uncertain nonlinear systems has become an active research field of international automatic control in recent years. With the development of related disciplines, it is of great theoretical significance to solve the problem that the conventional method is difficult to solve by using the advantages of fuzzy control. The main contents are as follows:In the first part, the research background and basic knowledge of this thesis are introduced.In the second part, an adaptive fuzzy control method is proposed for a class of uncertain nonlinear pure-feedback systems with dead zone output. Compared with the existing methods, this method uses the fuzzy system to approximate the unknown function of the system. Based on the Lyapunov stability, the results of the proposed method ensure that all signals of the closed-loop system are uniformly bounded and the tracking error estimates converge into a small neighborhood. The simulation results show the effectiveness and feasibility of the algorithm.In the third part, an adaptive fuzzy backstepping control scheme is proposed for the nonlinear pure-feedback systems with dynamic uncertainties in this paper by using the backstepping technique. The proposed control scheme not only guarantees that all the signals in the closed-loop system are semiglobally bounded, but also makes the tracking error converge to a small neighborhood of the origin by suitable choice of design parameters. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions. The primary characteristic of this thesis is that the unknown dead zone output nonlinearity and external disturbance of pure-feedback systems is introduced. Finally,an instances is used to prove the superiority of scheme.