Adaptive NN output-feedback decentralized stabilization for a class of large-scale stochastic nonlinear strict-feedback systems

In this paper, the decentralized adaptive neural network (NN) output-feedback stabilization problem is investigated for a class of large-scale stochastic nonlinear strict-feedback systems, which interact through their outputs. The nonlinear interconnections are assumed to be bounded by some unknown nonlinear functions of the system outputs. In each subsystem, only a NN is employed to compensate for all unknown upper bounding functions, which depend on its own output. Therefore, the controller design for each subsystem only need its own information and is more simplified than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the whole closed-loop system can be proved to be stable in probability by constructing an overall state-quartic and parameter-quadratic Lyapunov function. The simulation results demonstrate the effectiveness of the proposed control scheme. Copyright (C) 2010 John Wiley & Sons, Ltd.