DocumentCode :
2373433
Title :
A control scheme based on RBF Neural Network for High-precision Servo System
Author :
Hongjie, Hu ; Jinyu, Yue ; Ping, Zhan
fYear :
2010
fDate :
4-7 Aug. 2010
Firstpage :
1489
Lastpage :
1494
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.
Keywords :
Lyapunov methods; adaptive control; closed loop systems; feedback; feedforward; learning systems; neurocontrollers; radial basis function networks; servomechanisms; stability; 3-axis flying simulator; Lyapunov stability; RBF neural network; adaptive learning algorithms; closed-loop system; feedback controller; feedforward controller; high-precision servo system; modeling error; neural network controller; parameter disturbance; parameter variation; real-time position servo system; system-tracking stability; Adaptation model; Artificial neural networks; Compounds; Equations; Mathematical model; Servomotors; Torque;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation (ICMA), 2010 International Conference on
Conference_Location :
Xi´an
ISSN :
2152-7431
Print_ISBN :
978-1-4244-5140-1
Electronic_ISBN :
2152-7431
Type :
conf
DOI :
10.1109/ICMA.2010.5589241
Filename :
5589241
Link To Document :
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