Adaptive NN control of strict-feedback systems using ISS-modular approach

Beibei Ren, Shuzhi Sam Ge, Tong Heng Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
Pages4693-4698
Number of pages6
DOIs
StatePublished - 2007
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Conference

Conference46th IEEE Conference on Decision and Control 2007, CDC
CountryUnited States
CityNew Orleans, LA
Period12/12/0712/14/07

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  • Cite this

    Ren, B., Ge, S. S., & Lee, T. H. (2007). Adaptive NN control of strict-feedback systems using ISS-modular approach. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC (pp. 4693-4698). [4434132] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2007.4434132