Adaptive H infinity tracking control for a class of uncertain nonlinear systems using radial-basis-function neural networks

In this paper, we propose a novel adaptive H. tracking controller design for a class of nonlinear systems with uncertain system and gain function, which are unstructured (or non-repeatable) and state-dependent unknown nonlinear functions. Both the separation technique of continuous function and radial-basis-function (RBF) neural network are incorporated to approximate the uncertain system function. Systematic design procedure is developed for the synthesis of adaptive neural network tracking control with H. performance. The resulting closed-loop system is proven to be semi-globally uniformly ultimately bounded and the effect of the external disturbances on the tracking error can be attenuated to any prescribed level. In addition, the possible controller singularity problem in some of the existing adaptive control schemes met with feedback linearization techniques can be removed and the adaptive mechanism with only one learning parameterizations can be achieved. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. Finally, simulation results show the effectiveness of the control scheme. (c) 2006 Elsevier B.V. All rights reserved.