Please use this identifier to cite or link to this item:
Title: Neural-based direct adaptive control for a class of general nonlinear systems
Authors: Zhang, T.
Ge, S.S. 
Hang, C.C. 
Issue Date: 1997
Citation: Zhang, T.,Ge, S.S.,Hang, C.C. (1997). Neural-based direct adaptive control for a class of general nonlinear systems. International Journal of Systems Science 28 (10) : 1011-1020. ScholarBank@NUS Repository.
Abstract: A direct adaptive controller based on high-order neural networks (HONNs) is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The plant is assumed to be a feedback linearizable and minimum-phase system. Firstly, an ideal implicit feedback linearization control (IFLC) is established using implicit function theory. Then a HONN is applied to construct this IFLC to realize approximate linearization. The proposed controller ensures that the closed-loop system is Lyapunov stable and that the tracking error converges to a small neighbourhood of the origin. The requirements of an off-line training phase and the persistant excitation condition are eliminated. Simulation results verify the effectiveness of the proposed controller and the theoretical discussion.
Source Title: International Journal of Systems Science
ISSN: 00207721
Appears in Collections:Staff Publications

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

Page view(s)

checked on Jan 20, 2022

Google ScholarTM


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