| Summary: | This paper presents innovative reinforcement learning methods for automatically tuning the parameters of a proportional integral derivative controller. Conventionally, the high dimension of the Q-table is a primary drawback when implementing a reinforcement learning algorithm. To overcome the obstacle, the idea underlying the <i>n</i>-armed bandit problem is used in this paper. Moreover, gain-scheduled actions are presented to tune the algorithms to improve the overall system behavior; therefore, the proposed controllers fulfill the multiple performance requirements. An experiment was conducted for the piezo-actuated stage to illustrate the effectiveness of the proposed control designs relative to competing algorithms.
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