Parameter identification of hysteresis model of macro-micro composite actuator based on improved gray wolf algorithm
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摘要: 精确辨识磁滞模型参数是保证宏微复合驱动器位移跟踪精度的关键，针对传统灰狼算法（GWO）存在局部最小值和求解精度不高的缺陷，文章提出一种改进的灰狼算法（TGWO）。通过Singer混沌映射优化了灰狼个体的初始位置，以增加种群多样性；采用非线性收敛因子策略提高了局部开发度和全局搜索度；在种群位置迭代更新中引入动态权重更新和自适应更新策略。通过仿真和实验表明：该算法能有效可靠地辨识宏微复合驱动器磁滞模型的参数，平均相对误差为4.6%，拥有更高的精度和收敛性。
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关键词:
- 宏微复合驱动器 /
- Jiles-Atherton磁滞模型 /
- 改进的灰狼算法 /
- 参数辨识
Abstract: The accurate identification of hysteresis model parameters is the key to ensuring the displacement tracking accuracy of macro-micro composite actuators. To address the shortcomings of traditional grey wolf algorithm (GWO), which is prone to local optima and premature convergence, an improved grey wolf algorithm (TGWO) is proposed. Updating the initial position of gray wolf individuals based on Singer chaotic mapping to improve population diversity; Adopting a nonlinear convergence factor strategy to improve local development and global search performance; Introducing dynamic weight updating and adaptive updating strategies in the iterative updating of population positions. Simulation and experiments have shown that this algorithm can effectively and reliably identify the parameters of the hysteresis model of macro-micro composite actuators, The mean relative error is 4.6%, with higher accuracy and convergence. -
表 1 参数辨识结果
参数 取值范围 GWO TGWO α [0,0.1] 0 0.007 a/（×103） [3,15] 5.064 6.288 k/（×103） [1,8] 3.165 4.137 c [0,0.3] 0.002 0.272 Ms/（×105A/m） [1,9] 1.008 6.309 
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