A loading control strategy for electric load simulator based on new mapping approach and fuzzy inference in Cerebellar Model Articulation Controller

To improve surplus torque suppression and loading performance of electric load simulators, this paper presents a loading control strategy based on the new mapping approach and fuzzy inference scheme in the fuzzy Cerebellar Model Articulation Controller. The proposed mapping approach and fuzzy inference scheme in the fuzzy Cerebellar Model Articulation Controller, designed free from the mathematical model of system, comprises a mapping fuzzy Cerebellar Model Articulation Controller and a fuzzy inference controller, in which the former is the main controller. By introducing the new mapping approach in mapping fuzzy Cerebellar Model Articulation Controller, the proposed control strategy is actually a global network with local weight updating and its continuity has been enhanced. The fuzzy inference controller is used as a fuzzy compensator. As a torque controlled system, electric load simulator takes the loading error as the performance index. The results of dynamic simulation and experiments indicate that the proposed loading control strategy can achieve favorable control performance.