关键词: 参数辨识, 超磁致伸缩执行器, 遗传模拟退火算法

Abstract: Accurately identifying the model parameters may improve the control precision of giant magnetostrictive actuator output displacement. Aiming at the problem that parameters of giant magnetostrictive hysteresis nonlinear model cannot be identified accurately by a single algorithm, an improved genetic simulated annealing algorithm is proposed. The algorithm is an integration of genetic algorithm and simulated annealing algorithm. First, an optimal group is gained by using genetic algorithm with quick search ability, and then the whole group is adjusted by using the sudden jumping ability of annealing algorithm. Moreover, the optimum reserved strategy and dynamic step size search method are adopted in the algorithm. Then, the algorithm is used to identify parameters for the displacement hysteresis nonlinear model of giant magnetostrictive actuator. The results show that the algorithm has both advantages of genetic algorithm and simulated annealing algorithm. It not only has fast convergence speed, but also improves identification precision and the quality of the optimal solution. Experimental results show that the elongation values of model calculated and measured agree well and the relative error is about 3.85%. Therefore, the method can identify the model parameters conveniently and effectively.

Key words: Genetic simulated annealing algorithm, Giant magnetostrictive actuator, Parameter identification