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

Bo Yang,Meng Zhang,Fuhuang Liu

doi:10.1177/0020294018823028

Abstract

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.

Highlights

Electric load simulator is an important test equipment in the hardware-in-the-loop simulation of the flight control system under laboratory conditions
In the mapping fuzzy Cerebellar Model Articulation Controller (MFCMAC), we introduce a new mapping approach to further enhance the continuity of Fuzzy Cerebellar Model Articulation Controller (FCMAC) for better control performance
To verify the feasibility of MIFCMAC, the loading experiment is conducted on the test bed of electric load simulator in the laboratory

Summary

Introduction

Electric load simulator is an important test equipment in the hardware-in-the-loop simulation of the flight control system under laboratory conditions. Using the electric load simulator to replace the traditional field test is cost efficient, more flexible and easy to control. The inherent nonlinear factors and the interference of external surplus torque in electric load simulator make it difficult to achieve satisfactory control effect for conventional model-based control methods. The Cerebellar Model Articulation Controller (CMAC), proposed by Albus[1] in 1975, is widely used in data fusion,[2,3] intelligent control[4,5,6,7] and many other fields[8,9,10,11] for its simple architecture, quick learning convergence and inexistence of local minimum.[12,13,14] For simulating the human cerebellum, one drawback of CMAC is its binary input activating function.[15]

Methods

Results

Conclusion