Research On Fault-Tolerant Control And Event-Triggered Control For Strict-Feedback Nonlinear Systems

With the development of science and technology,the scale and complexity of the actual control system are increasing.The fault probability of system is also increasing,so the safety and reliability of the control systems has become one of the focuses of attention.The occurrence of any kind of failure may reduce the control performance or destroy the stability of the system,and even lead to some unpredictable losses.Therefore,how to design an effective fault-tolerant control(FTC)method to ensure the control performance and stability of the whole system is a significant research subject.It is the fact that many actual control systems contain nonlinear characteristics,and it is very difficult to obtain the accurate model of nonlinear systems.Therefore,it is of great significance to study the control problem of nonlinear systems with unknown nonlinearity.The emergence of neural network(NN)approximation technology provides an effective way to solve this problem.Based on NN technology,many control problems of nonlinear systems have been solved.On the other hand,with the development of network communication technology and computer technique,it is becoming a common way for modern control systems to transfer data through network.Because of the characteristics of the traditional time driven control method,it will cause the redundancy of transmission information and waste limited communication bandwidth resources.In recent years,the proposed eventtriggered control method can effectively reduce the amount of data transmission and the burden of communication network.At present,the study of fault-tolerant control and event-triggered control for nonlinear systems is still in development stage,and there still have many problems to be further solved.On the basis of the previous work,this dissertation is concerned on the adaptive control problem,fault-tolerant control problem and event-triggered control problem for nonlinear systems by using backstepping method,neural network technique,adaptive technology and event-triggered strategy.First,an adaptive prescribed performance faulttolerant control approach is designed for a class of nonlinear systems with unknown control direction and input quantization.Then,the problem of adaptive prescribed performance output feedback control for nonlinear systems with unknown control direction and input saturation is considered.Furthermore,an adaptive finite-time output feedback control method based on event-triggered scheme is proposed for a class of nonlinear systems with unmodeled dynamics.Moreover,an adaptive fault-tolerant controller is designed for large-scale nonlinear systems with input quantization.Finally,the problem of adaptive event-triggered output feedback control for interconnected nonlinear systems with input quantization is investigated.The developed theoretical results are applied to the twostage chemical reactor with delayed recycle streams,the spring-mass-damper system,the single-link robot manipulator model and the interconnected inverted pendulums model.Simulation results illustrate the effectiveness of the proposed control methods.The main contents are outlined as follows:Chapters 1-2 systematically analyze and summarize the development of the faulttolerant control and the event-triggered control.Preliminaries and research methods about the considered problem are also given.Chapter 3 investigates the problem of adaptive prescribed performance FTC design for a class of nonlinear time-delay systems with unknown control directions,actuator fault and input quantization.Neural networks(NNs)are employed to identify the unknown nonlinear functions and the Nussbaum function is used to deal with the unknown control directions.Then,a novel adaptive prescribed performance controller is designed to reduce the effects of actuator fault,input quantization,NNs approximation errors and disturbances.Compared with the existing results,a new error transformation method is presented,and the knowledge of the quantization parameters and the control directions are unknown in the control design.Furthermore,the proposed control scheme can guarantee the boundedness of all the closed-loop signals and the prescribed time-varying tracking performance.Finally,simulation results are given to demonstrate the effectiveness of the proposed control method.Chapter 4 presents an adaptive output feedback control approach for a class of nonlinear systems with unknown control direction,input saturation and tracking error constraint.The Nussbaum function is employed to address the unknown control direction and a state observer is constructed by NNs to estimate the unmeasurable states.A new error constraint transformation is proposed to guarantee that the tracking error satisfies the prescribed performance.Then,a novel adaptive prescribed performance NN control method is designed.It is proved that the designed controller can guarantee that all the signals in the closed-loop system are bounded and the tracking error satisfies performance constraint condition.Finally,simulations on two examples are performed to illustrate the efficiency of the proposed control method.Chapter 5 studies the problem of adaptive finite-time event-triggered tracking control for nonlinear systems with unmodeled dynamics.In the framework of dynamic output feedback control,NNs are used to handle the unmodeled dynamics and unknown nonlinear functions,and then a novel finite-time output feedback control strategy and a corresponding event-triggered mechanism are further proposed by employing the backstepping technique and finite-time stability criterion.It is proved through the Lyapunov theory that the closed-loop system is stable and the tracking property is guaranteed in finite time.Finally,the effectiveness of the proposed scheme is illustrated by some simulation results.Chapter 6 presents an adaptive decentralized tracking control scheme for large-scale nonlinear systems with input quantization and actuator faults.The nonlinearities,timevarying actuator faults and disturbance are assumed to exist unknown upper and lower bounds.Then,an adaptive decentralized fault-tolerant tracking control method is designed without employing backstepping technique and NNs.In the proposed control scheme,adaptive mechanisms are used to compensate the effects of unknown nonlinearities,input quantization,actuator faults and disturbance.The designed adaptive control strategy can guarantee that all the signals of each subsystem are bounded and the tracking errors of all subsystems converge asymptotically to zero.Finally,simulation results are provided to illustrate the effectiveness of the designed approach.Chapter 7 investigates the problem of event-triggered decentralized control for interconnected nonlinear systems with input quantization.A state observer is constructed to estimate the unmeasurable states,and the state-dependent interconnections are accommodated by presenting some smooth functions.Then by employing backstepping technique and NNs approximation capability,a novel decentralized output feedback control strategy and an event-triggered mechanism are designed simultaneously.It is proved through Lyapunov theory that the closed-loop system is stable and the tracking property of all subsystems is guaranteed.Finally,the effectiveness of the proposed scheme is illustrated by an example.Finally,the results of the dissertation are summarized and further research topics are pointed out.