Please use this identifier to cite or link to this item: https://doi.org/10.1109/CDC.2007.4434132
Title: Adaptive NN control of strict-feedback systems using ISS-modular approach
Authors: Ren, B. 
Ge, S.S. 
Lee, T.H. 
Issue Date: 2007
Source: Ren, B.,Ge, S.S.,Lee, T.H. (2007). Adaptive NN control of strict-feedback systems using ISS-modular approach. Proceedings of the IEEE Conference on Decision and Control : 4693-4698. ScholarBank@NUS Repository. https://doi.org/10.1109/CDC.2007.4434132
Abstract: In this paper, adaptive neural network control is investigated for a general class of strict-feedback systems using "ISS-modular" approach. The closed-loop system consists of two interconnected subsystems: the state error subsystem and the weight estimation subsystem. First, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors. Then, a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. Finally, the stability of the entire closed-loop system is guaranteed by the small-gain theorem. The "ISS-modular" approach avoids the construction of an overall Lyapunov function for the closed-loop system, and overcomes the controller singularity problem completely. The simulation studies demonstrate the effectiveness of the proposed control method. © 2007 IEEE.
Source Title: Proceedings of the IEEE Conference on Decision and Control
URI: http://scholarbank.nus.edu.sg/handle/10635/69209
ISBN: 1424414989
ISSN: 01912216
DOI: 10.1109/CDC.2007.4434132
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