NDLI: Comparison of feedback controllers for feedback-error-learning neural network control system with application to a flexible micro-actuator

Content Provider	IEEE Xplore Digital Library
Author	Sasaki, M. Kawafuku, M. Takahashi, K.
Copyright Year	1997
Description	Author affiliation: Dept. of Mech. & Syst. Eng., Gifu Univ., Japan (Sasaki, M.)
Abstract	A neural network approach to on-line learning control and real time implementation for a flexible micro-actuator is presented. The flexible micro-actuator is made of a bimorph piezo-electric high-polymer material (Poly Vinylidene Fluoride). The control scheme consists of a feedforward neural network controller and a fixed-gain feedback controller. This neural network controller is trained so as to make the output of the feedback controller zero. In the process, the neural network learns the inverse dynamics of the system. We make some comparisons between PID and LQG controllers for this neural network controller. Experimental and numerical results for the tracking control of a piezopolymer actuator are presented and they show that the feedback-error-learning neural network is effective in accurately tracking a reference signal.
Starting Page	4035
Ending Page	4040
File Size	561498
Page Count	6
File Format	PDF
ISBN	0780340531
ISSN	1062922X
DOI	10.1109/ICSMC.1997.637294
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive control Neural networks Control systems Actuators Microactuators Artificial neural networks Electronic mail Feedforward neural networks Three-term control Linear feedback control systems
Content Type	Text
Resource Type	Article

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