Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM
journal contributionposted on 18.05.2017, 13:29 authored by Zhao-Hua Liu, Jing Zhang, Shao-Wu Zhou, Xiao-Hua Li, Kan Liu
In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE.
This work was supported in part by the National Natural Science Foundation of China under Grant 61174140 and Grant 61203309, by the Research Fund for the Doctoral Program of Higher Education of China under Grant 20110161110035, and by Key Projects in the National Science and Technology Pillar Program under Grant 2012BAH09B00 and Grant 2012BAH09B02.
- Mechanical, Electrical and Manufacturing Engineering