Nonlinear adaptive control using neural networks and its application to CSTR systems

In this paper, adaptive tracking control is considered for a class of gener al nonlinear systems using multilayer neural networks (MNNs). Firstly, the existence of an ideal implicit feedback linearization control (IFLC) is est ablished based on implicit function theory. Then, MNNs are introduced to re construct this ideal IFLC to approximately realize feedback linearization. The proposed adaptive controller ensures that the system output tracks a gi ven bounded reference signal and the tracking error converges to an epsilon -neighborhood of zero with epsilon being a small design parameter, while st ability of the closed-loop system is guaranteed. The effectiveness of the p roposed controller is illustrated through an application to composition con trol in a continuously stirred tank reactor (CSTR) system. (C) 1999 Elsevie r Science Ltd. All rights reserved.