NDLI: A Neural-Network-Based Model Reference Speed Control for High Precision Motion Control Systems

Content Provider	IEEE Xplore Digital Library
Author	Hu, Hongjie Li, Dedi
Copyright Year	2008
Abstract	This paper developed a model reference control scheme by introducing a PI controller and RBF neural network (RBFNN) controller for speed control of high precision motion control systems. In the paper the RBF controller is able to online learn the unknown model dynamics, parameter variation and disturbance of the system. The model reference adaptive control (MRAC) scheme is used to give better solutions with online adaptation. By using a PI controller, the dynamic performance of the system is improved. This paper introduced a feedback parameter , which makes it easier to assign the poles of the system. Thus, it is feasible to preserve favorable model-following characteristics under various conditions. The effectiveness of the proposed control scheme is demonstrated by simulation. It is found that the proposed scheme can reduce the plant’s sensitivity to parameter variation and disturbance. High precision performance is obtained when given constant and sine wave disturbance at the same time.
Starting Page	236
Ending Page	240
File Size	457249
Page Count	5
File Format	PDF
ISBN	0769531148
DOI	10.1109/UKSIM.2008.6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-01
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Torque Nonlinear control systems Control systems Adaptive control neural network Motion planning Programmable control motion control model reference Velocity control Neural networks Automatic control online para-meter adaptation Motion control
Content Type	Text
Resource Type	Article

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