Distance based intelligent particle swarm optimization for optimal design of permanent magnet synchronous machine

Lee, Jin-Hwan; Song, Jun-Young; Kim, Jong-Wook; Kim, Yong-Jae

doi:10.1109/cefc.2016.7816094

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…MEC model offers a good compromise between accuracy and computational cost [13]. In contrast, FEM guarantees accurate result and takes huge computational burden [14]. However, when it comes to optimal design of electric machines such as motors, analyzing precise characteristic is essential, as most electric machines have nonlinear magnetic saturation.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

et al. 2021

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Motor design can be said as multi-modal optimization problem, as many performances should be considered. In addition, a time-consuming finite element method (FEM) is required for accurate analysis of the motor, and such computational burden becomes worse when the FEM is applied to multimodal optimization problem. In this paper, adaptive-sampling kriging algorithm (ASKA) is proposed to relieve the computation cost of multi-modal optimization problem. The ASKA utilizes kriging interpolation model with generated samples by Compact Search Sampling (CSS) and Exclusive Space-filling Method (ESM). The CSS improves the accuracy of the solutions by generating samples near the expected solutions, and the ESM guarantees the diversity of solutions by generating samples far from existing samples, avoiding solution-near area. Using CSS and ESM, the ASKA adjusts the number of samples effectively and reduces function call considerably. The superior performance of the ASKA was verified by mathematical test functions with complex objective function regions. To validate the feasibility of actual electric machines, the ASKA was applied to optimal design of permanent magnet assisted synchronous reluctance motors for electric vehicles and optimum design with diminished torque ripple is derived. INDEX TERMS Electric vehicles, kriging, multi-modal optimization, optimal design, permanent magnet assisted synchronous reluctance motor (PMa-SynRM)

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Section: Proposed Algorithmmentioning

confidence: 99%

Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

et al. 2021

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“…In addition, this algorithm improves performance by information sharing and competition with other particles in populations. However, when the particles are incorrectly chosen, it will stop flying once it reaches an optimal position condition 31‐35 . The SA‐PSO is introduced to revise this strategy, so it has fine adjustment ability and avoids to be trapped in local optimization.…”

Section: Decoupling Control For the Ccr‐bim Based On Sa‐pso Support Vector Machine Inversementioning

confidence: 99%

A decoupling control of composite cage rotor bearingless induction motor based on SA‐PSO support vector machine inverse

Yang

Sun

et al. 2021

Int Trans Electr Energ Syst

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Summary A composite cage rotor bearingless induction motor (CCR‐BIM) is a complicated system with multi‐variable, nonlinear, and strong coupling. The decoupling control among speed and flux linkage and radial displacement is the base of the stable operation based on the structure, the suspension force and the mathematical model of the CCR‐BIM. A decoupling control strategy based on simulated annealing particle swarm optimization (SA‐PSO) support vector machine (SVM) inverse is proposed for the CCR‐BIM. First, the state equation and reversibility of the system are analyzed using a CCR‐BIM mathematical model and a compounded pseudo‐linear system consisting of the inverse system constructed is decoupled into two displacement subsystems by nonlinear SVM in series with the original system, a speed subsystem, and a flux linkage subsystem. Second, the SA‐PSO is introduced to improve the precision of the prediction model. Finally, results of simulation and experimental verify that the proposed strategy not only achieves the decoupling control between the speed, radial displacement and flux linkage, but also achieves stable suspension and high performance of the CCR‐BIM.

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“…Since the cost

{\overset{J}{true}}_{i} false(k false)

is complex and non‐differentiable, using the traditional search techniques is difficult to solve the optimization problem. In the works of Lee et al 29 and Hojjati et al, 30 the population‐based optimization approaches provide good solutions to similar complicated problems. Inspired by the facts, the DE algorithm is utilized to solve the local optimization, then the control law u i can be worked out directly.…”

Section: Adaptive Mpc Strategymentioning

confidence: 99%

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Zhang

Sun

Deng

2020

Adaptive Control & Signal

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This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

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Distance based intelligent particle swarm optimization for optimal design of permanent magnet synchronous machine

Cited by 5 publications

References 1 publication

Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

A decoupling control of composite cage rotor bearingless induction motor based on SA‐PSO support vector machine inverse

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

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