Please use this identifier to cite or link to this item:
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.
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
ISSN: 00051098
DOI: 10.1016/j.automatica.2006.01.004
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Feb 28, 2018


checked on Feb 21, 2018

Page view(s)

checked on Feb 27, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.