| With the development of science and technology,the scale and complexity of engineering control systems are increasing,which increases the chances of faults.The occurrence of any kind of faults may lead to a decline in overall system performance and even affect the system stability,which could result in some unpredictable losses.Therefore,it is particularly important to improve the safety and reliability of control systems.The emergence and development of the fault-tolerant control(FTC)provide an effective way to solve this problem.Considering the fact that the practical engineering systems are almost nonlinear systems,it is very meaningful to study the fault-tolerant control problem of nonlinear systems.Due to the complexity of nonlinear systems,the control theory for nonlinear systems is not perfect,and the fault-tolerant control methods for general nonlinear systems are not so rich.Moreover,most of the existing results mainly investigate the stability of faulty systems,and on this basis the problem of the system performance optimization is rarely considered.In recent years,the control methods for nonlinear systems based on the neural network approximation have attracted a wide spread attention.The introduction of the neural network learning method has greatly promoted the development of the nonlinear system control theory.However,most of the existing control methods for nonlinear systems are applicable to systems with specific structures,and there is almost such a limit for the researches on the fault tolerant control problems for nonlinear systems.Under the framework of optimal control and differential game theory,this thesis studies the optimal fault-tolerant control of nonlinear system based on the ADP method,fault-tolerant control,combined with fault-tolerant control theory,adaptive control,backstepping control,intelligent control and other modern control methods.Furthermore,the problem of optimal fault-tolerant control for a single nonlinear system is extended to the adaptive optimal fault-tolerant control problem of multi-agent systems.Then,the adaptive FTFC problem of UAVs is studied when one or more UAV fails.Finally,considering the disturbance problems that may occur in the actual situation,the optimal cooperative fault-tolerant control algorithm for UAVs under bounded interference is studied.The main work is summarized as follows.(1)A novel fault tolerant control scheme for a class of affine nonlinear systems with actuator failures based on adaptive dynamic programming is presented.A novel adaptive scheme is developed to estimate the fault parameters,and the estimated actuator failure is utilized to construct an improved performance index function,so the FTC problem can be transformed into an optimal control problem.By using policy iteration,the Hamilton–Jacobi–Bellman equation can be solved by constructing a critic neural network.Then,the approximated optimal controller can be derived directly.The closed-loop system is guaranteed to be asymptotically stable via the Lyapunov stability theorem.(2)A feedforward + feedback control architecture is proposed for a class of nonlinear systems in strict feedback form in the presence of partial loss of actuator effectiveness faults is proposed,using backstepping design technique and adaptive dynamic programming algorithm.First,based on the backstepping method,the feedforward control input is given,in which the actuator fault is overcome by introducing an auxiliary dynamic system and Lyapunov function,respectively.Thus,the FTC problem of original system is converted into an optimal regulation control problem of the equivalent tracking error dynamic system.Subsequently,the critic network is built to approximate the optimal cost function online by using ADP technique.Since the fault estimation and control law parameters are updated online,the control system has an adaptive failure compensation capability so as to reconfigure the control law in real time in response to failure indications.(3)In order to counteract actuator faults in formation flight of multiple unmanned aerial vehicles,this paper presents a distributed optimal fault-tolerant formation control strategy,which is derived via adaptive dynamic programming technique.The proposed controller can be divided into two parts:(1)optimal control policy of the fault-free systems and(2)fault compensator.Firstly,a model-based policy iteration algorithm is introduced to obtain the optimal control law in which a critic network is constructed to approximate the associated cost function online with a designed weight update law.Subsequently,a fault compensator is derived to get rid of the impact of the actuator fault.Therefore,the whole controller,not only guarantees all signals in the UAVs system are uniformly ultimately bounded,but also guarantees the cooperative cost function is minimized.For the UAV formation system,the superiority of using a single network rather than the typical dual network structure of ADP is more prominent due to it can not only reduce the memory requirements but also alleviate the computational burden of the UAV.(4)Considering the fact that there may be external disturbance in practice,the differential gamebased optimal cooperative fault-tolerant control problem of multi-agent systems with external disturbance is studied.Firstly,the dynamic of local neighborhood consensus error for fault-free UAVs system is derived based on graph theory and consensus theory.So,the anti-interference problem of UAVs is transformed into the optimal formation control problem of a class of nonlinear MAS,and the adaptive differential game-based cooperative control strategy with external disturbance is derived.Then,a fault compensator is derived to get rid of the impact of the actuator fault.Moreover,based on the actor-critic control structure,new weight tuning laws are given to learn the nearly differential gamebased distributed optimal FTFC control policy.Finally,the boundedness of the closed-loop signals is proved based on the Lyapunov stability theory. |
PDF Full Text Request