Adaptive hysteresis compensation control of a macro-fiber composite bimorph by improved reinforcement learning.

ADAPTIVE control systems; BOUC-Wen model; PID controllers; HYSTERESIS; ALGORITHMS

The hysteresis characteristics related to the frequency and amplitude of the control signal seriously affect the precision of the displacement tracking control of the macro-fiber composite (MFC) bimorph. The traditional feedforward compensator tends to exhibit low precision in controlling displacement. Although it enhances the control accuracy to a certain extent by incorporating a feedback controller, the existing feedback controller has a weak adaptive ability. Thus, the Q-learning (QL) algorithm is combined with the Bouc-Wen (BW) feedforward compensator in this study. The output voltage from the BW compensator has a more significant impact on the control accuracy and convergence speed of the QL algorithm. Therefore, this paper proposes an improved QL (IQL) algorithm that leverages the control error. Moreover, a BW-IQL adaptive control method is proposed to realize precise adaptive control of the MFC bimorph. Experimental comparisons are performed with the proposed method and the traditional BW, BW-PID, and BW-fuzzy PID controllers. The BW-IQL controller reduces the average relative errors of the three controllers by 86.9%, 59.9%, and 41.8%, respectively. Meanwhile, the Q̄ table plays a major role in error suppression in the IQL controller. These results verify that the BW-IQL control method has higher adaptability and accuracy. [ABSTRACT FROM AUTHOR]

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