Systems, Man & Cybernetics - January 2016 - 26

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An Enhanced
Approach
for Parameter
Estimation
Using Immune Dynamic Learning Swarm Optimization
Based on Multicore Architecture
by Zhao-Hua Liu, Xiao-Hua Li,
Hong-Qiang Zhang,
Liang-Hong Wu, and Kan Liu

T

he identification of physical parameters is
crucial for control-system designs, condition
monitoring, and fault diagnosis of industrial
drive systems. This article brings multicorearchitecture-based parallel computing technology and bioinspired intelligent optimization algorithm

Digital Object Identifier 10.1109/MSMC.2015.2472915
Date of publication: 6 June 2016

26

IEEE SyStEmS, man, & CybErnEtICS magazInE Janu ar y 2016

insight into designing for system parameter estimation
models. In this study, a parallel implementation using an
immune-cooperative dynamic learning particle swarm
optimization (PSO) algorithm with multicore computation
architectures is presented for permanent magnet synchronous machine (PMSM) parameter estimations. Three novel
strategies are discussed, all with the purpose of enhancing
the dynamic response and fast convergence performance
of the designed parameter estimator. The strategies include
a dynamic velocity modification strategy, an immune-memory-based searched information preservation mechanism,
and an immune-network-based learning operator for PSO.
Finally, a proposed method is applied to the parameter estimations of PMSMs as well as parallel running on multicore
2333-942X/16©2016IEEE


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Table of Contents for the Digital Edition of Systems, Man & Cybernetics - January 2016

Systems, Man & Cybernetics - January 2016 - Cover1
Systems, Man & Cybernetics - January 2016 - Cover2
Systems, Man & Cybernetics - January 2016 - 1
Systems, Man & Cybernetics - January 2016 - 2
Systems, Man & Cybernetics - January 2016 - 3
Systems, Man & Cybernetics - January 2016 - 4
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Systems, Man & Cybernetics - January 2016 - 43
Systems, Man & Cybernetics - January 2016 - 44
Systems, Man & Cybernetics - January 2016 - Cover3
Systems, Man & Cybernetics - January 2016 - Cover4
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