Research On Adaptive Fuzzy Control Methods For Nonlinear Systems

Adaptive fuzzy control is an important control method. It can successfully controlnonlinear systems with model uncertainties. Actually, most systems in practice are nonlinear, so adaptive fuzzy control method can be used widely. Meanwhile, there may be some faults unavoidable during the operation of the controlled system. Therefore, faulttolerant control has also attracted much attention. On the basis of adaptive fuzzy control method, this dissertation presents robust control and fault-tolerant control schemes for nonlinear systems with model uncertainties. The research on fault-tolerant control is focused primarily on the loss of effectiveness and being stuck of actuators. Besides, deadzone control is also a valuable research topic, because there are dead-zone phenomena in many physical systems. Consequently, this dissertation also proposes a new dead-zone model, and a novel adaptive fuzzy dead-zone control approach as well.Based on the Lyapunov stability theory and the universal approximation property offuzzy logic systems, a robust output tracking controller, some fault-tolerant controllers and a dead-zone controller are designed in this dissertation for nonlinear systems with model uncertainties. Because most existing methods require the fuzzy approximators with known basis functions and there may be large on-line computation burden in some of them, a new adaptive controller based on nonlinearly parameterized fuzzy approximators is also proposed, which can remove the restriction that the fuzzy basis functions of the approximators must be known and reduce the on-line computation burden effectively. The designed controllers can guarantee the stability of the closed-loop system and satisfactory output performance as well. The main contributions of this dissertation are the ones as follows:Firstly, on the basis of adaptive fuzzy approximation method and the Lyapunov stability theory, an indirect adaptive fuzzy robust control scheme is proposed for nonlinear systems satisfying matching conditions. In this scheme, an identification model of the controlled system is constructed by using the same adaptive fuzzy approximators in thecontroller, then better tracking and approximation performances can be obtained by employing the identification error to improve the adaptive laws of the on-line adjustable parameters. It resolves the problem of the tradeoff between control effort and disturbance attenuation level on some extent. The effectiveness of the proposed scheme is proved in theory and convinced by a simulation example.Secondly, a fault-tolerant output regulation controller and a fault-tolerant output tracking controller are designed for some classes of nonlinear systems satisfying matching conditions. Though there are model uncertainties and actuator faults in the controlled systems, the designed controllers can make the closed-loop systems tolerate the occurred faults and realize the stability and desired control effect of the closed-loop systems. By using piecewise analysis method, it is proved in theory that the designed controllers are effective. The obtained conclusions are further verified by simulation results.Thirdly, based on adaptive fuzzy approximation method, the fault-tolerant control problem is studied for some strict-feedback nonlinear systems with model uncertainties. By employing backstepping design technique, the restriction that the controlled systems need to satisfy the matching conditions is removed, adaptive fuzzy fault-tolerant control method is developed for uncertain strict-feedback nonlinear systems. When there are external disturbances in the controlled systems, additional control is included to deal with the external disturbances and the fuzzy approximation error together. Then, a faulttolerant controller is designed for a class of nonlinear multi-input multi-output systems with model uncertainties. By taking integral Lyapunov functions, the designed controller can tolerate time-varying actuator faults. The given simulation examples demonstrate the feasibility of the proposed control schemes.Fourthly, a class of strict-feedback nonlinear systems with model uncertainties and time-varying perturbed dead-zones is studied. A new dead-zone model is proposed. The dead-zone described by this model can be time-varying and perturbed. Based on this deadzone model, a novel adaptive fuzzy dead-zone control method is proposed. By analyzing the dead-zone property as a linear-like term, a nonlinear term and a disturbance-like term, the method can derive a controller which is less conservative than some existing ones, because less control efforts are needed for dealing with the disturbance-like term. The derived controller can guarantee the stability and good output tracking performance of the closed-loop system. The given simulation example demonstrates the effectiveness of the proposed method.Fifthly, the problem of the control design based on nonlinearly parameterized fuzzy approximators is considered for strict-feedback nonlinear systems with model uncertainties. A novel adaptive fuzzy control method is presented. The method does not require the basis functions of the fuzzy approximators known, which makes a big difficulty for deriving the adaptive laws for unknown parameters because they are nonlinear. By expanding estimate errors into Taylor series and some manipulations, the nonlinearity problem of the unknown parameters can be solved, thus the adaptive laws can be designed based on the Lyapunov stability theorem. Besides, the designed adaptive laws update the norm of the unknown parameter vectors, which reduces the on-line computation burden of the controller. Simulation results show that the control objective can be achieved by the proposed method.Finally, the results of this dissertation are summarized and further research topics are also pointed out.