1384308

11372

In this paper, adaptive tracking control is considered for a class of general nonlinear systems using multilayer neural networks (MNNs). Firstly, the existence of an ideal implicit feedback linearization control (IFLC) is established based on implicit function theory. Then, MNNs are introduced to reconstruct this ideal IFLC to approximately realize feedback linearization. The proposed adaptive controller ensures that the system output tracks a given bounded reference signal and the tracking error converges to an "- neighborhood of zero with " being a small design parameter, while stability of the closed-loop system is guaranteed. The e€ective- ness of the proposed controller is illustrated through an application to composition control in a continuously stirred tank reactor (CSTR) system.

313

Nonlinear systems , Input±output feedback linearization , multilayer neural networks , CSTR , Adaptive control

Studia Iranica

323

323