摘要
永磁直线电机的优化是一个多维的非线性问题,本文将永磁体等效为磁化电流,对磁化电流傅里叶变换来构建永磁直线电机模型,根据模型推导出目标函数。对于电机参数的优化,本文采用改进的粒子群算法来优化直线电机,在优化过程中增加了遗传算法中的交叉这个过程,实现了粒子群算法与遗传算法相结合,这种改进的算法不仅能减少迭代次数,让目标函数快速收敛,还能防止陷入局部最优,提高算法寻找全局最优的可靠性。
The optimization of permanent magnef linear motor is a multidimensional nonlinear problem. U sing magnetizing current to instead of permanent magnet, this article built the permanent magnet linear motor model about the armature exciter current and the Fourier transform of magnetization current. According to the model, derived the objective function. This paper used the Particle swarm optimization algorithm to optimize parameters of linear motor. In the optimize process, added the process of cross over which was always in ge netic algorithm, and achieved the combination of Particle swarm optimization algorithm and genetic algo rithm. This algorithm can reduce the number of iterations to make the objective function converging quickly, and prevent failing into local optimum and improve the reliability of finding the global optimum.
出处
《微电机》
北大核心
2012年第10期1-3,37,共4页
Micromotors
基金
国家自然科学基金项目(61075071)
河南自然科学基金(2010A470006)
河南自然科学基金(2011A470010)
郑州市科技局项目(10PTGG380-1)
关键词
粒子群算法
电机优化
等效电流
气隙磁密
Particle swarm optimization algorithm
optimization of motor
equivalent current
air gapflux density
