Adaptive Control For Two Classes Of Nonlinear Non-strict Feedback Systems

Because the practical system usually has nonlinear characteristics,which seriously affects the stability of the system,so the research on nonlinear system has very important practical value.At present,adaptive intelligent control method has become one of the mainstream methods to solve the control design problems of complex nonlinear systems.Based on the research of adaptive control methods at home and abroad,this paper studies the design methods of the two kinds of controllers and the convergence and stability of the closed-loop system by using the combination of backstepping method and adaptive control method.The main content of this paper is divided into the following two parts:1.The problem of adaptive fuzzy tracking control for a class of uncertain non strict feedback nonlinear systems with time delay and unknown virtual control coefficients is studied.In this scheme,fuzzy logic systems(FLSs)are used to approximate uncertain nonlinear functions.Backstepping technique and Lyapunov stability theory are used to design adaptive fuzzy controller.The number of on-line adaptive parameters of the control scheme does not exceed the order of the original system,which is the advantage of the scheme.The results show that the proposed adaptive fuzzy controller can ensure that all signals of the closed-loop system are bounded and the output of the system can track the desired reference signal.Finally,the simulation results verify the effectiveness of the proposed control scheme.2.Adaptive neural control is studied for uncertain stochastic nonlinear systems with non-strict feedback form.In this study,radial basis function neural networks(RBF NNs)are used to estimate uncertain nonlinear systems.According to the combination of backstepping control and adaptive control method,an adaptive neural network tracking controller is proposed.The well-designed adaptive neural network controller ensures that all signals in the closed-loop system are semi globally uniformly bounded,and the tracking error eventually converges to a small neighborhood near the origin.The simulation results illustrate the feasibility of the proposed controller.