Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators

Authors:
Jiaju Zheng;Shuying Cao;Hongli Wang;Wenmei Huang
Affiliations:
School of Mechanical Engineering, Tianjin University, Tianjin 300072, China;School of Electrical Engineering and Automation, Hebei University of Technology, Tianjin 300130, China;School of Mechanical Engineering, Tianjin University, Tianjin 300072, China;School of Electrical Engineering and Automation, Hebei University of Technology, Tianjin 300130, China
Venue:
Neurocomputing
Year:
2007

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Abstract

In this paper, we present an improved hysteresis model for magnetostrictive actuators. To obtain optimal parameters of the model, we study two distinct hybrid strategies: namely, employing a gradient algorithm as a local search operation of a genetic algorithm (GA), and taking the best individual of a GA as the initial value of a gradient algorithm. Here, two different gradient algorithms, a well-known Levenberg-Marquardt algorithm (LMA) and a novel Trust-Region algorithm (TRA), are investigated. Finally, the proposed four hybrid genetic algorithms (HGAs) are applied to identify parameters of the improved model. The simulation and experimental results show the performances of the HGAs and the improved hysteresis model.