Michael Ray, Yves C. Martin
Proceedings of SPIE - The International Society for Optical Engineering
This paper presents a learning self-tuning (LSTR) regulator which improves the tracking performance of itself while performing repetitive tasks. The controller is a self-tuning regulator based on learning parameter estimation. Experimentally, the controller was used to control the movement of a nonlinear piezoelectric actuator which is a part of the tool positioning system for a diamond turning lathe. Experimental results show that the controller is able to reduce the tracking error through the repetition of the task. © 1993 by ASME.
Michael Ray, Yves C. Martin
Proceedings of SPIE - The International Society for Optical Engineering
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Theoretical Computer Science
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INTERSPEECH - Eurospeech 2001
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