Abstract:Accurately identifying the hysteresis model parameters may improve the control precision of giant magnetostrictive actuator output displacement. The single algorithm is difficult to achieve accurate identification of the ultramagnetically induced nonlinear model parameters. This paper presents a new hybrid optimization strategy, the modified genetic algorithm and simulated annealing and applied to identification of the giant magnetostrictive actuator displacement hysteresis model parameters. The algorithm taking into account the genetic algorithm and simulated annealing algorithm strengths, and also introduces machine learning theory, simulated annealing algorithm as the population variation in genetic algorithm operator and simulated annealing algorithm and genetic algorithm Metropolis sampling process combines. This algorithm not only give full play to the ability of genetic algorithms parallel search features, but also enhance and improve the ability of genetic algorithms evolution and improve the convergence and convergence speed of the system, to avoid losing the optimal solution. The simulation and experimental results show that the algorithm with respect to the genetic algorithm has a high accuracy, the parameters can effectively identify the model.