Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Journal Article > Item 987654321/91651

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/91651

Title:	Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Authors:	Hsu, Chun-Fei
Contributors:	淡江大學電機工程學系
Keywords:	Hermite neural network;Adaptive control;Neural control;Pendulum;Robotic manipulator
Date:	2013-05-01
Issue Date:	2013-07-23 21:46:57 (UTC+8)
Publisher:	Amsterdam: Elsevier BV
Abstract:	This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.
Relation:	Applied Soft Computing 13(5), pp.2569–2576
DOI:	10.1016/j.asoc.2012.11.029
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

Files in This Item:

File	Description	Size	Format
ASOC_1811.pdf		1255Kb	Adobe PDF	322	View/Open
index.html		0Kb	HTML	295	View/Open