Study On Adaptive Control For Nonlinear Uncertain Switched Systems

With the development of modern industrialization,most systems have unknown parameters,component faults,environmental disturbances and other factors,which make system modeling nonlinear and uncertain.Therefore,the control problem of uncertain nonlinear systems has received extensive attention.Fuzzy control provides an effective approach to settle this problem.In addition,adaptive backstepping control technology plays an important role in the control of nonlinear systems with parameter uncertainties,especially for complex nonlinear systems with unknown functions.Based on the adaptive Backstepping design method,this thesis presents adaptive fuzzy control schemes for two classes of uncertain nonlinear switched systems.Fuzzy logic systems and radial basis function neural networks are using to approximate the unknown nonlinear functions.The developed control strategy ensures the stability of the closed-loop system.The details are as follows:Chapter 3 considers an adaptive tracking control issue for a class of uncertain switched stochastic nonlinear systems.Based on Lyapunov stability theorem,two classes of state feedback controllers are constructed by adopting the adaptive backstepping technique.The first controller is designed under multiple adaptive laws.Then,the second one is designed based on constructing a maximum common adaptive parameter,which can overcome the problem of over-parameterization of the first controllers.Both of these controllers can ensure that all the signals in the closed-loop system are bounded,and the tracking error converges to a small neighborhood of the origin.Finally,two simulation results show that the proposed control scheme is effective.Chapter 4 considers an adaptive finite-time tracking control issue for switched nonlinear systems with unknown hysteresis.Based on finite time stability criterion,an adaptive neural network control scheme is proposed.Under the presented controller,the desired system performance is realized in finite time.Finally,the validity of the system is verified by a simulation example.