Neural-network-based Tracking Control Of Switched Nonlinear Systems With Input Delays

Switched systems can describe the behaviour of a large number of practical plants resulting from the interactions of continuous dynamics,discrete dynamics,and logic decisions,etc.In this thesis,for several kinds of switched nonlinear systems,the design method of the controller,the boundedness,and the convergence problem of the resulting closed-loop systems are studied by using Backstepping design method and neural network based adaptive control method.The main work of this thesis is summarized as follows:A neural-network-based control scheme is developed for the tracking control problem of a class of switched strict-feedback nonlinear systems with uncertain input delay and external time-varying disturbances.First,the auxiliary signals are obtained by masterly constructing a filter and a virtual observer.Then the adaptive Backstepping technique and neural network(NN)are employed to construct a common Lyapunov function(CLF)and a state feedback controller for all subsystems.It is proved that all signals of the closed-loop system are semi-globally uniformly ultimately bounded(SGUUB),and that the tracking error ultimately converges to an adequately small compact set.Finally,The effectiveness of the proposed method is verified by a simulation example.For a class of disturbed multiple input multiple output(MIMO)uncertain nonlinear switched systems with input delay,proposes a new neural-network-based adaptive output tracking control scheme.By combining radial basis function(RBF)neural networks' universal approximation ability and adaptive Backstepping recursive design with an improved multiple Lyapunov function(MLF)scheme,a novel adaptive neural output tracking controller design method is presented for the switched system.It is proved that all signals of the closed-loop system are SGUUB,and that the tracking error ultimately converges to an adequately small compact set.Finally,the effectiveness of the proposed method is verified by a simulation example.