Parameter identification of strain hysteresis model for giant magnetostrictive actuators using a hybrid genetic algorithm

This paper shows a hysteresis model of giant magnetostrictive actuator (GMA), and proposes a hybrid genetic algorithm (HGA) to identify the parameters of the model. In the HGA, the trust region algorithm (TRA) is taken as a local search operator which parallels to the selection, crossover and mutation operators of a float-coded genetic algorithm (FCGA). The HGA is paid attention to both the advantages of the TRA and the genetic algorithm. It not only has a rather high convergence speed, but also can rind the best parameter with a rather large probability. The simulation and experimental results verify the effectiveness of the model and the HGA.