The control of active magnetic bearing using two-phase Q-learning

Abstract eng:
Q-learning method proved to be usable in active magnetic bearing (AMB) control task, however the learning speed remains the main problem. Two-phase variant of the Q-learning speeds up the learning process by dividing the method into two stages - pre-learning stage which uses mathematical model of AMB and is highly efficient and tutorial stage which uses conventional Q-learning and is used for further fining of the Q-function. Comparison with PID controller is included to demonstrate algorithms control abilities. As table (step function) is used as Q-function implementation, the optimum configuration of the table (number and relative positions of node points) was also investigated.

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