An intelligent CMAC-PD torque controller with anti-over-learning scheme for electric load simulator

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The hybrid architecture of a cerebellar model articulation controller (CMAC) and proportional derivative (PD) can effectively reduce the loading error and restrain the surplus torque of electric load simulators. However, due to the over-learning problem of the CMAC, the practical application of the CMAC-PD for electric load simulators is greatly constrained. This paper analyses the over-learning problem of CMAC and proposes a novel learning scheme including control error and an inhibiting over-learning item. An intelligent CMAC-PD controller with novel anti-over-learning scheme is derived by using the gradient descent algorithm. Both simulation and experimental results demonstrate that the proposed CMAC-PD hybrid controller has good robustness, can effectively eliminate disturbances and restrain the over-learning phenomenon of the CMAC.