Direct Adaptive Neural Network Control of a Class of Nonlinear Systems

This paper focuses on adaptive neural network control for a class of uncertain single-input single-out nonlinear strict-feedback systems. Neural network (NN) is directly used to approximate the unknown desired control signals and a novel direct adaptive neural network controller is proposed via backstepping and the minimal learning parameter (MLP) techniques. The main advantages of the proposed controller are that: (1) the problem of "explosion of complexity" inherent in the conventional backstepping method is avoided; (2) the problem of "dimensionality curse" is solved and only one adaptive parameter that needs to be updated online. These advantages result in a much simpler adaptive control algorithm, which is convenient to implement in applications. The proposed controller guarantees that all the close-loop signals are uniform ultimate boundedness (UUB) and that the tracking errors converge to a small neighborhood of the desired trajectory. Finally, simulation studies are given to show the effectiveness of the proposed approach.