Adaptive neural control for a class of large-scale pure-feedback nonlinear systems

Abstract

This paper considers the problem of adaptive neural decentralized control for pure-feedback nonlinear interconnected large-scale systems. Radical basis function (RBF) neural networks are used to model packaged unknown nonlinearities and backstepping is used to construct decentralized controller. The proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. A numerical example is provided to illustrate the effectiveness of the suggested approach. © 2013 Springer-Verlag Berlin Heidelberg.