Adaptive Neural Network Control for a Class of Non-affine Nonlinear System Based on Fuzzy Estimator

In this paper, the adaptive neural network control is investigated for a class of SISO non-affine nonlinear dynamic systems. The proposed method obtains the adaptive laws directly employing the gradient descent algorithm to minimize the control error between the unknown ideal controller and the NN controller. A fuzzy inference system (FIS) with heuristically defined rules is introduced to provide an estimate of this error based on the past history of the system behavior. We prove that the closed-loop system is stable and all signals are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme.