Adaptive neural network control of nonlinear systems by state and output feedback

This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN's), Firstly, under the conditions of the system output and its time derivatives being available for feedback, an ad aptive state feedback NN controller is developed, When only the output is m easurable, by using a high-gain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closed-loop system is proven to be semi-globally uniformly ultimately bound ed (SGUUB). In addition, if the approximation accuracy of the neural networ ks is high enough and the observer gain is chosen sufficiently large, an ar bitrarily small tracking error can be achieved. Simulation results verify t he effectiveness of the newly designed scheme and the theoretical discussio ns.