Development of a Novel Self‐Tuned Adaptive Supervisory Cerebellar Model Articulation Controller for Induction Motor Drive

Abstract

AbstractThis work presents a novel speed control scheme for an induction motor (IM) using an adaptive supervisory differential cerebellar model articulation controller (ASDCMAC). The ASDCMAC has a supervisory controller and an adaptive differential cerebellar model articulation controller (ADCMAC), and the ASDCMAC is utilized as the speed controller. The supervisory controller monitors the control process to keep speed tracking error within a predefined range, and the ADCMAC learns and approximates system dynamics. The connective weights of ADCMAC are adjusted online, according to adaptive rules derived in Lyapunov stability theory, to ensure system stability. The robustness of the proposed ASDCMAC against parameter variations and external load torque disturbances is verified via simulations and experiments, respectively. Three control schemes, the ASDCMAC, fuzzy control, and PI control, are investigated experimentally, and a performance index, root mean square error (RMSE), is utilized for each scheme. The experimental results demonstrate that the ASDCMAC outperforms the two other control schemes with external load torque variations.