2013
DOI: 10.4028/www.scientific.net/amm.416-417.395
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Multiobjective Design Optimization of Ironless Permanent Magnet Linear Synchronous Motors for Improved Thrust and Reduced Thrust Ripple

Abstract: In order to achieve high thrust density and low thrust ripple simultaneously, a multiobjective optimization design method is applied to ironless permanent magnet linear synchronous motor with non-overlapping windings. On the basis of the magnetic field analysis model, the analytical formulae of key parameters such as No-load back EMF, thrust per copper quality and thrust ripple was deduced. Further, a multiobjective optimization is carried out by genetic algorithm to search for the optimal design variables. Th… Show more

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Cited by 4 publications
(3 citation statements)
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“…Where F MRD is the total damping force of MR damper, c is the viscous damping coefficient, k is the stiffness coefficient, a is the scale factor of the hysteresis loop, z hysteretic operator, in which b is the slope of the hysteresis loop, d is the width coefficient of hysteresis loop, f 0 is the offset damping force. Multi-population genetic algorithm (MGA) (Li et al, 2013) was used to identify the parameters of hyperbolic tangential model. This algorithm can effectively restrain the phenomena of premature convergence, poor local search ability, and late population assimilation of the standard genetic algorithm.…”
Section: Dynamic Model Of Mr Dampermentioning
confidence: 99%
See 1 more Smart Citation
“…Where F MRD is the total damping force of MR damper, c is the viscous damping coefficient, k is the stiffness coefficient, a is the scale factor of the hysteresis loop, z hysteretic operator, in which b is the slope of the hysteresis loop, d is the width coefficient of hysteresis loop, f 0 is the offset damping force. Multi-population genetic algorithm (MGA) (Li et al, 2013) was used to identify the parameters of hyperbolic tangential model. This algorithm can effectively restrain the phenomena of premature convergence, poor local search ability, and late population assimilation of the standard genetic algorithm.…”
Section: Dynamic Model Of Mr Dampermentioning
confidence: 99%
“…Multi-population genetic algorithm (MGA) (Li et al, 2013) was used to identify the parameters of hyperbolic tangential model. This algorithm can effectively restrain the phenomena of premature convergence, poor local search ability, and late population assimilation of the standard genetic algorithm.…”
Section: Dynamic Modeling Of Mr Dampermentioning
confidence: 99%
“…Byungkwan et al [8] proposed an adaptive particle swarm optimization algorithm for reluctance motor optimization, which converged faster than the standard particle swarm optimization algorithm and had an improved ability to search for a better solution set. Li et al [9] used a multiple swarm genetic algorithm with a strong search capability for the multi-objective optimization of the thrust density, thrust fluctuation, and loss using the dimensions of the permanent magnets and toroidal windings of a coreless permanent magnet linear synchronous motor as variables. It was verified that the optimal design results obtained by MPGA were in good agreement with the design objectives under different weight coefficients.…”
Section: Introductionmentioning
confidence: 99%