超磁致伸缩执行器位移模型的参数辨识

Parameter Identification of Displacement Model for Giant Magnetostrictive Actuator

准确辨识模型参数是提高超磁致伸缩执行器位移控制精度的关键,针对单一算法难以实现对超磁致伸缩磁滞非线性模型参数准确识别的问题,将遗传算法与模拟退火算法融合,首先利用遗传算法的快速搜索能力得到一个较优群体,再利用模拟退火算法的突跳能力对整个群体进行优化调整,并在算法中引入最优保留策略和动态步长搜索方法,提出一种改进的遗传模拟退火算法,并将其应用于对超磁致伸缩执行器位移磁滞非线性模型参数辨识。该算法兼具遗传算法和模拟退火算法的优点,既有较快的收敛速度,又提高了辨识精度和最优解质量。通过试验验证,超磁致伸缩棒伸长量的模型计算结果与测量值符合程度较好,平均相对误差为3.85%,该方法能方便有效地辨识模型参数。

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.