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Journal of Control Science and Engineering
Volume 2012, Article ID 867178, 9 pages
http://dx.doi.org/10.1155/2012/867178
Research Article

Robust Adaptive Control via Neural Linearization and Compensation

Departamento de Control Automatico, CINVESTAV-IPN, Avenue.IPN 2508, 07360 Mexico City, DF, Mexico

Received 6 October 2011; Revised 4 January 2012; Accepted 5 January 2012

Academic Editor: Isaac Chairez

Copyright © 2012 Roberto Carmona Rodríguez and Wen Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.