Adaptive neural network control for strict-feedback nonlinear systems using backstepping design

This paper focuses on adaptive control of strict-feedback nonlinear systems using multilayer neural networks (MNNs). By introducing a modified Lyapuno v function, a smooth and singularity-free adaptive controller is firstly de signed for a first-order plant. Then, an extension is made to high-order no nlinear systems using neural network approximation and adaptive backsteppin g techniques. The developed control scheme guarantees the uniform ultimate boundedness of the closed-loop adaptive systems. In addition, the relations hip between the transient performance and the design parameters is explicit ly given to guide the tuning of the controller. One important feature of th e proposed NN controller is the highly structural property which makes it p articularly suitable for parallel processing in actual implementation. Simu lation studies are included to illustrate the effectiveness of the proposed approach. (C) 2000 Elsevier Science Ltd. All rights reserved.