Intelligent Adaptive Reconfigurable Control For Complex Nonlinear System

The intelligent adaptive reconfigurable control for a class of complex nonlinear dynamic system with uncertainty and (or) time-delay is studied in this dissertation. Based on advanced robust control techniques and combined with T-S fuzzy model and adaptive neural network, a set of reconfigurable control methods are proposed for complex nonlinear system.First, the problem of the fuzzy H state feedback control for a class of uncertain nonlinear systems with input and state time delay is addressed. The uncertain Takagi-Sugeno (T-S) fuzzy model with time delay is adopted for fuzzy modeling of nonlinear system, and the systematic design procedures for the fuzzy robust controller design are given.Based on T-S fuzzy model, the fuzzy fault tolerant control design for actuator failures in nonlinear system is proposed. By the proposed control scheme, the stability of the system is maintained with an acceptable level of tracking performance when an actuator blocked or outage.Further the backstepping technique is adopted to construct fuzzy robust reconfigurable control scheme for a class of uncertain nonlinear system, which is an integration of fuzzy-mode-based controller and fuzzy adaptive controller. Fuzzy adaptive controller in this scheme is used to cancel the effect caused by the modeling error, disturbance, and unknown actuator failures.In order to overcome the disadvantage of the current fuzzy model-based control design, a new control method is introduced in an effort to combine adaptive neural network (NN) design with fuzzy T-S model-based control methodology. A full adaptive RBF neural network is added to the fuzzy H control in order to guarantee the robust stability of the controlled system. The effect of the unknown uncertainties and the error caused by fuzzy modeling can be overcome by adaptive tuning of the weights, centers and widths of the RBF neural network on line. The stability and tracking control problem of the uncertain nonlinear system is solved when the nonlinear uncertainty and fuzzy modeling error does not satisfy so-called matching condition.When a common positive definite matrix can not be found to design fuzzy model-based controller, the improved adaptive reconfigurable control scheme based on fuzzy model and neural network is proposed. The overall control scheme is constructed by combining all local state feedback controllers and robust adaptive controllers which based on the adaptive neural network. The proposed fuzzy adaptive robust controller is developed without finding a common positive definite matrix to satisfy a matrix Lyapunov equation, which extend the applicable scope of this method.Furthermore, due to the symmetry restriction of traditional radial basis function networks (RBFN) with Gaussian function, the asymmetric Gaussian basis function (AGBF) is proposed to construct the full adaptive AGBFN. Because the asymmetry Gaussian function's variability and malleability are higher than the traditional one, the asymmetry Gaussian basis function can provide the AGBFN which own a higher flexibility and can approach the true result more easily.At last, the proposed methods are successfully applied to the simulation of fighter flight control system, results of simulation test show the effectiveness of the proposed methods.