The Optimization Design Of Low-speed Permanent Magnet Synchronous Linear Motor Based On Improved Ant Colony Algorithm

Low-speed permanent magnet synchronous linear motor is a new motor whichwill be able to get the low-speed linear running without intermediate conversiondevices. So if it’s well designed, this kind of motor will be widely used in thecircumstances of low-speed linear running with high working efficiency, which willprove the necessity of studying how to optimize this kind of motor. In the meantime,there’s no systematic and effective way to solve the optimizing problem because of itscomplicated structure and magnetic field.Nowadays, the concept of energy saving has been generally accetped by people,so the power index of the motor, undoubtedly, has been the focus of the researchers.This paper aims to optimize the low-speed permanent magnet linear motor in order toimprove the power index, namely thrust volume.Firstly, the paper introduces the background, significance, the current status ofresearch of this field, and then it shows the basic structure and work principle of thelow-speed permanent magnet synchronous linear motor, and indicates its mathematicmodel. Secondly, by comparison of respective performances of several kinds ofpermanent magnet material commonly used, this paper selects NdFeB permanentmagnet materials as permanent magnets of the motor, derives someelectromagnetic-related expressions of the motor, and designs the magnetic circuitand its armature. After that, with the aim of optimizing the motor’s power index, thepaper builds an optimized mathematic model, which includes derivation of theobjective function, setting the constraints, and selections of optimized variables, andit selects the improved ant colony algorithm as the way of optimization throughcomparison of some widely-used ones. This paper presents in detail the principle,mathematic model, solving steps. With basic ant colony algorithm and the methods ofadaptive penalty function, the paper designs an improved ant colony algorithm whichcan solve continuous space constrained optimization problems, and through solvingtwo examples with programs, high solving efficiency is proved on problems ofcontinuous space constrained optimization; In the end, the improved algorithm is used to solve the motor optimization model it builds, and by comparison, theimproved have higher volume ratio of thrust than the former ones, which reaches thegoal of optimization.