Design of Adaptive Sliding Diagonal Recurrent Cerebellar Model Articulation Controller for Direct Torque Control Systems of an Induction Motor

Abstract

A novel adaptive sliding diagonal recurrent fuzzy cerebellar model articulation controller (ASDRC) is proposed by this study. ASDRC includes the inputs by a sliding surface into a diagonal recurrent fuzzy cerebellar model articulation controller (DRCMAC). The ASDRC enables cerebellar model articulation controller (CMAC) to exhibit both static and dynamic characteristics, indicating that the use of DRCMAC improves the disadvantage of conventional CMAC while exhibiting the advantages of fuzzy CMAC (FCMAC). Regarding the proposed ASDRC, the adaptive update law determining the memory weights, means of Gaussian functions, and standard deviations of Gaussian functions is yielded by the Lyapunov stability theory; moreover, the gradient descent method is applied to yield the recurrent weight update law. Using the adaptive update law and recurrent weight update law of the ASDRC to implement online adjustment ensures system stability. To demonstrate the performance of the proposed ASDRC, this study applies it to the direct torque control (DTC) systems of an induction motor to perform experiments. The root-mean-square error is used as an assessment indicator to compare the results of the proposed controller with those of the FCMAC. The experimental results prove that the ASDRC has more excellent response, and its performance is superior to that of the FCMAC.