Abstract
This study proposed an adaptive recurrent cerebellar model articulation controller (ARCMAC) which consisted of a recurrent Gaussian cerebellar model articulation controller (RCMAC) and a compensation controller. The ARCMAC was used as the speed controller for a switched reluctance motor (SRM), and the contained compensation controller was used to offset the error between the RCMAC and a theoretically idea control. Lyapunov theory was used in this study to derive the update laws of the weights of the ARCMAC, the weights of recurrence, and the parameters of Gaussian functions to ensure the stability of controlled system.
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