Identification of prandtl-ishlinskii hysteresis models using modified particle swarm optimization

Authors:
Mei-Ju Yang;Guo-Ying Gu;Li-Min Zhu
Affiliations:
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China;State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Year:
2012

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Abstract

A modified particle swarm optimization algorithm (MPSO) is proposed in this paper. The algorithm is implemented to identify the parameters of the hysteresis nonlinearity, which is described by a modified Prandtl-Ishlinskii model. This new algorithm redefines the global best position and personal best position in the traditional PSO algorithm by an effective informed strategy, in order to balance the exploitation and exploration of the algorithm. Furthermore, a mutation operator is employed to increase the diversity of the particles and prevent premature convergence. Experiments have been conducted to verify the effectiveness of the proposed method. The comparisons with other variants of the PSO demonstrate that the identification of hysteresis based on the MPSO is effective and feasible.