State Constraints-Based Adaptive Control Design For Switched Nonlinear Systems

In recent society,the control systems become increasingly complex and the requirement of control performance and effect become increasingly higher.In addition,due to the fact that many practical systems can be modeled as switched systems in nature or on account of complex control objectives,such as switching power converters,transportation systems and mechanical systems,switched systems have obtained a considerable amount of research effort,and different effective control schemes have been developed.On the other hand,because of inherent limits of devices or technological requirements,in the operation space,most of physical systems must handle some limits or constraints.For example,the angle position and angle velocity of an one-link robot arm must be under restrictions because of technological requirements and site limitations.Therefore,the research on the problem of state constraints has great practical significance.Based on this,combing with the current research status of state-constraints based switched nonlinear systems,the problems of stabilization and tracking control are investigated by exploiting switched systems theory,backstepping,dynamic surface control,the common Lyapunov function method,the multiple Lyapunov function method,the average dwell time method and event-triggered control,and the state constraints are not violated.The main results of this thesis are summarized as follows:(1)For a class of switched nonlinear systems with unmodeled dynamics,a stateconstraints based adaptive neural network tracking control problem is studied.First,a switching dependent dynamic signal is designed to dominate unmodeled dynamics.Also,by introducing a nonlinear mapping,the limited state variables are transformed into new state variables without constraints in the switched systems framework,which permits removal of feasibility conditions for virtual control signals,when applying the barrier Lyapunov function or integral barrier Lyapunov function schemes.Second,adaptive neural network controllers of individual subsystems are constructed by exploiting backstepping and an improved average dwell time method with dwell time reset,which guarantee that all signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded under a class of switching signals with average dwell time and full state constraints are not violated,and furthermore,the tracking error converges to a small neighborhood of the origin.Finally,two examples,which include a two inverted pendulums as a practical example,are provided to demonstrate the applicability and effectiveness of the proposed designed method.(2)For a class of switched strict-feedback nonlinear systems,a state-constraints based adaptive event-triggered tracking control problem is studied.First,by introducing a nonlinear mapping,the limited state variables are transformed into new state variables without constraints in the switched systems framework,which permits removal of feasibility conditions for virtual control signals,when applying the barrier Lyapunov function or integral barrier Lyapunov function schemes.Second,adaptive event-triggered controllers of all subsystems are constructed by exploiting dynamic surface control and common Lyapunov function,which guarantee that all signals in the resulting closed-loop system are semi-globally uniformly ultimately bounded under a class of switching signals with mini dwell time and full state constraints are not violated,and furthermore,the tracking error converges to a small neighborhood of the origin,the Zeno behavior is excluded.Finally,two examples,which include an one-link manipulator system as a practical example,are provided to demonstrate the applicability and effectiveness of the proposed designed method.The conclusions and perspectives are presented in the end of the thesis.