Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71652
Title: Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems
Authors: Ge, S.S. 
Wang, J. 
Issue Date: 2002
Citation: Ge, S.S.,Wang, J. (2002). Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems. Proceedings of the World Congress on Intelligent Control and Automation (WCICA) 1 : 77-82. ScholarBank@NUS Repository.
Abstract: This paper presents a robust adaptive neural control approach for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearitis comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks; while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions". It has been proven that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals. Simulation studies show the effectiveness of the proposed approach.
Source Title: Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
URI: http://scholarbank.nus.edu.sg/handle/10635/71652
Appears in Collections:Staff Publications

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

Page view(s)

22
checked on Jul 6, 2018

Google ScholarTM

Check


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