Combined Genetic Algorithm Control for Bearingless Motor Suspended System

Abstract

Genetic algorithm is an efficient global optimal searching algorithm. It is based on the natural selection and the heredity theory, and combined the principle of the fittest survival with the stochastic exchange of community internal chromosome. This paper takes bearingless motor as control object, makes the simulation in Matlab and use real-time emulation system dspace to experiment about the bearingless motor suspended system. The result demonstration that as increase iterative number of times the result parameter get more and more perfect to the suspension control. With the end of evolution, the algorithm finds the best control parameters. The algorithm is optimal globally, effect significantly to the suspension system, so it fitted for complex and non-linear bearingless motor about the suspension control.