NDLI: A control scheme based on RBF Neural Network for High-precision Servo System

Content Provider	IEEE Xplore Digital Library
Author	Hu Hongjie Yue Jinyu Zhan Ping
Copyright Year	2010
Abstract	In this paper, a novel control scheme based on RBF Neural Network is proposed for High-precision Servo System. The aim of this study is to reduce the influence which arises from modeling error, unknown model dynamics, parameter variation and disturbance acted on the practical system and to achieve high tracking precision. This scheme consists of a Neural Network controller (NNC), a Feedforward controller and a Feedback controller. All adaptive learning algorithms in this study are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop whether the uncertainties occur or not. In order to improve the tracking performance, a feedforward controller is added, whose parameters are obtained when nominal model's parameters are fixed. With the selection of the poles of system, parameters of the Feedback controller could be determined. Experiment results on 3-axis flying simulator verify the proposed strategy can achieve high tracking precision for real-time position servo system.
Starting Page	1489
Ending Page	1494
File Size	252065
Page Count	6
File Format	PDF
ISBN	9781424451401
ISSN	2152744X
DOI	10.1109/ICMA.2010.5589241
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Mathematical model Equations Adaptation model Compounds Servomotors Torque
Content Type	Text
Resource Type	Article

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