Zhou Miaolei, Wang Shoubin, Gao Wei
College of Communication Engineering, Jilin University, Changchun 130022, China.
ScientificWorldJournal. 2013 Apr 28;2013:865176. doi: 10.1155/2013/865176. Print 2013.
As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing. Compared with traditional actuators, MSMA actuator has the advantages as fast response and large deformation; however, the hysteresis nonlinearity of the MSMA actuator restricts its further improving of control precision. In this paper, an improved Krasnosel'skii-Pokrovskii (KP) model is used to establish the hysteresis model of MSMA actuator. To identify the weighting parameters of the KP operators, an improved gradient correction algorithm and a variable step-size recursive least square estimation algorithm are proposed in this paper. In order to demonstrate the validity of the proposed modeling approach, simulation experiments are performed, simulations with improved gradient correction algorithm and variable step-size recursive least square estimation algorithm are studied, respectively. Simulation results of both identification algorithms demonstrate that the proposed modeling approach in this paper can establish an effective and accurate hysteresis model for MSMA actuator, and it provides a foundation for improving the control precision of MSMA actuator.
作为一种新型智能材料，磁形状记忆合金（MSMA）在执行器制造应用中具有良好的性能。与传统执行器相比，MSMA执行器具有响应速度快、变形大等优点；然而，MSMA执行器的滞后非线性限制了其控制精度的进一步提高。本文采用改进的克拉索夫斯基-波克罗夫斯基（KP）模型建立MSMA执行器的滞后模型。为了识别KP算子的加权参数，本文提出了一种改进的梯度校正算法和一种变步长递推最小二乘估计算法。为了验证所提建模方法的有效性，进行了仿真实验，分别研究了改进梯度校正算法和变步长递推最小二乘估计算法的仿真。两种辨识算法的仿真结果表明，本文所提建模方法能够为MSMA执行器建立有效且准确的滞后模型，为提高MSMA执行器的控制精度奠定了基础。
