Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.automatica.2006.01.004
Title: An ISS-modular approach for adaptive neural control of pure-feedback systems
Authors: Wang, C.
Hill, D.J.
Ge, S.S. 
Chen, G.
Keywords: Adaptive neural control
Input-to-state stability
Non-affine systems
Pure-feedback systems
Small-gain theorem
Issue Date: May-2006
Source: Wang, C., Hill, D.J., Ge, S.S., Chen, G. (2006-05). An ISS-modular approach for adaptive neural control of pure-feedback systems. Automatica 42 (5) : 723-731. ScholarBank@NUS Repository. https://doi.org/10.1016/j.automatica.2006.01.004
Abstract: Controlling non-affine non-linear systems is a challenging problem in control theory. In this paper, we consider adaptive neural control of a completely non-affine pure-feedback system using radial basis function (RBF) neural networks (NN). An ISS-modular approach is presented by combining adaptive neural design with the backstepping method, input-to-state stability (ISS) analysis and the small-gain theorem. The difficulty in controlling the non-affine pure-feedback system is overcome by achieving the so-called "ISS-modularity" of the controller-estimator. Specifically, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors, and a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. The stability of the entire closed-loop system is guaranteed by the small-gain theorem. The ISS-modular approach provides an effective way for controlling non-affine non-linear systems. Simulation studies are included to demonstrate the effectiveness of the proposed approach. © 2006 Elsevier Ltd. All rights reserved.
Source Title: Automatica
URI: http://scholarbank.nus.edu.sg/handle/10635/55043
ISSN: 00051098
DOI: 10.1016/j.automatica.2006.01.004
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