Full State Constraints-Based Adaptive Control Design For Switched Nonlinear Systems

With the rapid development of modem industry,the requirement to the performance of control systems is more and more higher,which results in that the traditional linear theory cannot satisfy the actual requirements.Then,the nonlinear control method has received more attention,due to the needs of the practical application and theoretical development.On one hand,because of inherent limits of devices,most of physical systems must handle some limits or constraints in the operation space.In other words,constraints are ubiquitous in many areas of system analysis.Thus,in most physical control problems,handling constraints is one of the fundamental problems.On the other hand,in the last decade,switched systems as an important class of hybrid systems,have attracted a great deal of attention.This is largely because that many practical systems,such as power systems,transportation systems and mechanical systems,can be modeled as switched systems.Switched systems theory and backsteeping and the Barrier Lyapunov function method are used to study the problem of state constraints of switched nonlinear systems,which obviously is an effective way.However,almost no results on such issue have been reported by now.This thesis mainly investigates the problem of adaptive state feedback and output feedback control for switched nonlinear systems with full state constraints.The main contributions are as follows:First of all,the problem of full state constraints-based adaptive control for a class of switched nonlinear pure-feedback systems under arbitrary switching is investigated.The switched pure-feedback system is transformed into a switched strict-feedback system with non-affine terms based on the mean value theorem.Then,by exploiting the common Lyapunov function(CLF)method,the Barrier Lyapunov function method and backstepping,state feedback controllers of individual subsystems and a common Barrier Lyapunov function(CBLF)are constructed,which guarantee that all signals in the closed-loop system are global uniformly bounded under arbitrary switching,and full state constraints are not violated.Furthermore,the tracking error can converge to a bounded compact set.A single-link robot as a practical example,is provided to demonstrate the effectiveness of the proposed design method.Second,for a class of switched uncertain nonlinear systems,the problem of full state constraints-based adaptive output feedback control under arbitrary switching is studied.The unknown functions are approximated directly by neural networks and the states of switched systems are estimated by constructing a switched K filter.Then,by combining the CLF method and the BLF method,an improved backstepping method is provided.Finally,the proposed output feedback controllers of subsystems guarantee that all closed-loop signals remain bounded under arbitrary switching,while the output tracking error converges to a small neighborhood of the origin.Also,full state constraints are not violated.Simulation results demonstrate the effectiveness of the proposed approach.The conclusions and perspectives are presented in the end of the thesis.