Adaptive neural control for a class of switched nonlinear systems

In this paper, adaptive neural control is presented for a class of switched nonlinear systems with switching jumps and uncertainties in both system models and switching signals. Condition oil limiting variation of the Lyapunov function is given for input-to-state asymptotic stability of switched systems with switching jumps. The control objective is achieved uniformly with respect to a class of switching signals. The coupled difficulties from the discrepancy between control gains and switching jumps are overcome by discontinuous adaptive neural control combined with the classical adaptive control. Smooth approximations of the discontinuous controls are then presented for a systematic design procedure. (C) 2008 Elsevier B.V. All rights reserved.