Please use this identifier to cite or link to this item:
Title: Adaptive neural control of uncertain multi-variable nonlinear systems with saturation and dead-zone
Authors: Chen, M. 
Ge, S.S. 
How, B.V.E. 
Issue Date: 2009
Citation: Chen, M.,Ge, S.S.,How, B.V.E. (2009). Adaptive neural control of uncertain multi-variable nonlinear systems with saturation and dead-zone. Recent Advances in Intelligent Control Systems : 195-222. ScholarBank@NUS Repository.
Abstract: In this chapter, adaptive neural control is developed for a class of uncertain MIMO nonlinear systems using neural networks. The MIMO system under study is a strict-feedback uncertain nonlinear system with non-symmetric input nonlinearities. Variable structure control (VSC) technique in combination with backstepping is proposed to tackle the input saturation and dead-zone. The spectral radius of the control coefficient matrix is introduced to design adaptive neural control in order to cancel the nonsingular assumption of the control coefficient matrix. Using the cascade property of system, the semi-global uniform ultimate boundedness of all signals in the closed-loop system is achieved. The tracking errors converge to small residual sets which are adjustable by updating design parameters. Finally, case study results are presented to illustrate the effectiveness of the proposed adaptive neural control. © 2009 Springer London.
Source Title: Recent Advances in Intelligent Control Systems
ISBN: 9781848825475
DOI: 10.1007/978-1-84882-548-2_9
Appears in Collections:Staff Publications

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


checked on Mar 25, 2023

Page view(s)

checked on Mar 16, 2023

Google ScholarTM



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