Engineering Mechanics

Proceedings Vol. 12 (2006)

ENGINEERING MECHANICS 2006

Copyright © 2006 Institute of Theoretical and Applied Mechanics, Academy of Sciences of the Czech Republic, Prague

 

M. Turek, T. Březina, J. Pulchart
pages 392 - +10p., full text

The active magnetic bearing control through self learning controller is described in this contribution. Controller’s coefficient (parameter) values come from actions of Continuous Action Reinforcement Learning Automatas (CARLAs) which continuously update the controller’s coefficients according to behavior of the active magnetic bearing. The goal of this on-line training is formulated as achievement of minimum mean square of control error. It is shown that CARLA method is capable of learning better parameters than standard method of optimal control design called LQ (linear quadratic) design. Described concept of control is proved by control of the active magnetic bearing.

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