Optimal Design Based on Genetic Algorithm and Characteristic Test for Giant Magnetostrictive Actuator

This paper presented the optimization design model of giant magnetostrictive actuator (GMA) and applied the multi-object genetic algorithm for the optimization design of its solenoid coil and enamelled wire, and tested the static characteristic and dynamic characteristic of GMA here. The optimal objects of the model included: the structure design of GMA, the method building of producing bias magnetic field, study for the magnetic field distribution along the axis and maximization for magnetic field density of coil, from which the solenoid coil parameter was optimally designed. The optimization variables included: the size of GMM rod, the structure parameter of coil, the power consumption of solenoid coil and the enamelled wire parameter. The domain of optimal variables was determined according to the demand for application. The fittest parameters of coil were obtained using non-dominated sorting genetic algorithm (NSGA) combining with BP neural network (BPNN) by the search in multi-objective parameters space. The result of experiment incarnates fine static and dynamic characteristic of GMA and consistency between design parameter and experimental value, which shows the rationality of the optimal design.