超磁致伸缩作动器非线性模型辨识研究

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (18) : 142-146.

论文

超磁致伸缩作动器非线性模型辨识研究

杨理华 1 李践飞1 吴海平1 楼京俊2

作者信息 +

Researches on Parameter Identification of Nonlinear Model for Giant Magnetostrictive Actuator

Yangli Hua1, LI Jian-fei1,WU Hai-ping1, LOU Jing-jun2

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文章历史 +

摘要

准确辨识超磁致伸缩作动器非线性模型参数是位移精确控制的必要条件，针对标准粒子群(PSO)算法存在早熟收敛及迭代后期易陷入局部最优的不足，本文提出一种可动态调整惯性权重、学习因子及带遗传变异的改进型粒子群(IPSO)辨识算法，该算法可平衡全局和局部搜索能力，提高收敛速度和辨识精度，并将该算法应用于超磁致伸缩作动器非线性模型的参数辨识研究。结果表明：该算法能有效可靠地辨识超磁致伸缩作动器非线性模型参数，计算值和实验的吻合程度较高，并且具有一定的抑噪能力。

Abstract

Accurate identification of nonlinear model parameters is a prerequisite to precisely control precise displacement of giant magnetostrictive actuator, Aiming at the shortcomings of standard PSO algorithm such as existing premature convergence and easily falling into local optimum in later iteration. An improved PSO identification algorithm is proposed in this paper, which can dynamically change inertia weighting, study factors and genetic variation, so it can balance global and local search capability to improve the convergence speed and identification accuracy. Moreover, it is applied to parameters identification of nonlinear model for giant magnetostrictive actuator. Then the results show that: the improved algorithm can effectively identify the nonlinear model parameters of giant magnetostrictive actuator. It also has a higher degree of agreement between calculations and experiments and a better anti-interference ability.

导出引用

杨理华 1 李践飞1 吴海平1 楼京俊2. 超磁致伸缩作动器非线性模型辨识研究[J]. 振动与冲击, 2015, 34(18): 142-146

Yangli Hua1, LI Jian-fei1,WU Hai-ping1, LOU Jing-jun2. Researches on Parameter Identification of Nonlinear Model for Giant Magnetostrictive Actuator[J]. Journal of Vibration and Shock, 2015, 34(18): 142-146

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