Adaptive Control For Uncertain Nonstrict-feedback Switched Nonlinear Systems

In recent years,because nonlinear systems can be used to describe many practical applications,the control problems of nonlinear systems have been widely concerned.It is noted that,as a typical hybrid system,switched nonlinear system can model the practical system more accurately and effectively based on its multimodal characteristics.In addition,uncertainty inevitably exists in many engineering systems,and neural network has a good approximation effect,which is one of the important ways to handle the control problem of uncertain nonlinear systems.Approximation-based adaptive backstepping control for nonlinear systems has become one of the most important topics in control science and engineering.Therefore,by combining approximation-based adaptive backstepping technique and Lyapunov stability theory,two konds of adaptive neural network controller are designed to deal with the state-feedback and output-feedback tracking control problems of uncertain switched nonlinear systems with nonstrict-feedback structure.The main works of the study are as follows:(1)This thesis is focused on the issue of adaptive neural state-feedback control for a class of uncertain switched single input and single output(SISO)nonlinear systems with unknown backlash-like hysteresis input and dead-zone output.A Nussbaum-type gain function is presented in this thesis to overcome the difficulty existing in tracking the dead-zone output with unknown control direction.Then,an adaptive neural state-feedback tracking controller is designed by combining the variable separation technique with the common Lyapunov function method.Finally,the simulation results show that the designed controller can guarantee that all the signals are bounded and the tracking error converges to a small neighborhood of the origin.(2)The problem of adaptive neural output-feedback tracking control for a class of output constrained switched multi-input and multi-output(MIMO)nonstrict-feedback nonlinear systems with unknown dead-zone input is addressed.First,neural networks-based switched observer is set up to approximate all the unmeasurale states.Meanwhile,an addition adaptive parameter is adopted to tackle the nonstrict-feedback form.Then,in the backstepping framework,construct multiple Lyapunov functions for the whole switched nonlinear systems,and the dynamic surface control scheme is applied to remove the issue of “explosion of complexity”existing the traditional backstepping framework.Finally,the stability of the considered system is analysed based on average dwell time(ADT)approach,and the feasibility of the proposed control strategy is proved by simulation results.