Intelligent Fault Diagnosis And Tolerant Control For Complex Nonlinear System

The intelligent fault diagnosis and active tolerant control for a class of complex uncertain nonlinear dynamic systems or model-unkown time delay systems with unmeasured states are studied in this dissertation. Based on the states observer design theory, robust control theory , adaptive control theory and combined with advanced intelligent techniques including the neural network and T-S fuzzy model, a set of fault diagnosis and reconfigurable control methods are proposed.The main contents of the dissertation are as follows:At first, the observability definitions for the nonlinear system are given. A state observer design method for a class of uncertain nonlinear systems whose reletive degree equals the system order n is addresed.The system is transformed diffeomorphically into a canonical system with the modelling error only depending on the measurable input and output data. When the systems reletive degree is less its orders, a new observer structure is introduced, and a neural network is applied to approximate the uncertainty . The observer guarantees that the state estimate error converges to zero provided that system zero dynamics is asymptotically stable.Based on estimated states, a fault diagnosis architecture for a class of uncertan nonlinear syetems is proposed, a diffeomorphism is applied to transform the nonlinear system into a new coordinate system . The estimated states are input to the fault approximator whose outputs are estimated fault model.When a system model is unknown and the states are unavailable for measurement, the states are estimated on-line by employing a general RBF neural network, while the fault of system is estimated by an adaptive RBF neural network where center and width vectors of Gaussian function are on-line updated. Further the fault diagnosis scheme for a class of nonlinear time-delay systems with unmeasured states is studied. Unlike the usual method which is within framework of Linear Matrix Inequalities techniques(LMI) , in this paper,the estimated states and time-delay states are used the input to the neural networks in order to approximate the fault model.The stability of the error system are analyzed using Lyapunov stability theory.A robust adaptive tracking control architecture with state observer is proposed for a class of nonaffine nonlinear systems. A high-gain observer is used to estimate the derivatives of system output, a RBF neural network is used to cancel nonlinear uncertainties. Applying estimate states , the track controller is designed , the fixed control law and adaptive law are derived. It is shown that the tracking error is guaranteed to be asymptotically convergent with the aid of an additional robustifying control term when there not exists externel disturbance, and a tracking perfomance is achieved with a externel disturbance. A magnetic levitated ball system is used as a simulation example.Furthemore, the active fault-tolerant control techniques against actuator failure are investigated. One fault-tolerant control method is studied based on model reference adaptive control and Takagi-Sugeno(T-S) model. Usurally, the nonlinear system is modelled by T-S model on the condition that the system matrix parameters of local linear model are known . But , the matrix parameters of local linear model are unknown in this paper, therefore the controller parameters are adjusted on-line in order to make the system tracking the reference model. After fault occurrence, the tolerant control laws are reconfigured to cancel the effects of the failed actuators. The matching conditions and the controller adjusting laws are given. Another fault-tolerant control method is proposed based on iterative learning observer and T-S fuzzy model . After fault concurrent, the iterative learning observer estimates the system states and fault stuck value,while T-S fuzzy model is used to model and control nonlinear system. The feedback controller is constructed using the estimated states and the fault information. Matching conditions for achieving tolerant control are given. The performance is evaluated using the aircraft models.