Adaptive Control Of Pure Feedback Nonlinear Systems With Input/State Constraints

In practical engineering control systems,many controlled systems can be modeled as pure feedback nonlinear systems,such as single link manipulator,rocket system and aircraft system.Therefore,the study of control methods for pure feedback nonlinear systems has not only theoretical value but also practical significance.Due to the constraints of physical conditions,the system is often subject to some constraints.If the constraints cannot be met,the system may be unstable,or even cause system collapse.Therefore,the control problem of nonlinear systems with conditional constraints has become one of the research focuses in the nonlinear field.So far,although there have been many valuable achievements in the research of pure feedback nonlinear systems,there are still some problems to be solved.Based on this,this thesis studies the control problem of pure feedback nonlinear systems with state constraints in detail by combining adaptive control methods with backstepping methods and fuzzy logic systems.The main work and innovation of this thesis are as follows:(1)An adaptive fuzzy control method is proposed for pure feedback stochastic nonlinear systems with input saturation and partial state constraints.Firstly,the mean value theorem is used to decouple the pure feedback nonlinear system;Secondly,the barrier Lyapunov function(BLF)is introduced to ensure that the constrained partial system states do not violate the constraint conditions;Then,the smooth nonlinear function is introduced to approximate the input saturation function,which solves the influence of input saturation;Fuzzy logic systems are used to approximate unknown terms in nonlinear systems.The proposed control method guarantees that all signals of the closed loop system are bounded without violating the constraints.(2)An adaptive fault-tolerant control(FTC)method is proposed for pure feedback switched nonlinear systems with actuator failures and full state constraints.Firstly,the mean value theorem is used to decouple the pure feedback nonlinear system;Secondly,fuzzy logic systems are used to approximate the unknown terms in nonlinear systems;Then,using the common Lyapunov function(CLF)method and the backstepping method,a common BLF is constructed for each subsystem to ensure that all closed-loop signals are bounded and the system state does not violate the constraint conditions under any switching.In addition,the adaptive law designed in this method reduces the computational complexity significantly.(3)For the control method mentioned above,MATLAB is used to simulate and analyze the second-order pure feedback system,single-link robot and second-order pure feedback switching system.The simulation results show that both control methods can ensure that the system output can accurately track the reference signal,the adaptive law can quickly approximate the unknown items in the system,and the system state does not violate the constraint conditions,and under the fault-tolerant control,the system output can tolerate the impact of actuator failure,with a certain degree of robustness.