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基于MGPI模型的SMA柔性驱动自适应NN控制
引用本文:冯颖,梁明威.基于MGPI模型的SMA柔性驱动自适应NN控制[J].控制理论与应用,2022,39(4):721-729.
作者姓名:冯颖  梁明威
作者单位:华南理工大学,华南理工大学
基金项目:国家自然科学基金项目(62073146), 广东省自然科学基金项目(2018A030313331, 2021A1515011851), 广州市科技计划基础与应用基础研究项目 (202102080435)资助.
摘    要:形状记忆合金(SMA)作为一类仿人肌肉驱动的智能柔性驱动材料,在机器人及高端制造等领域逐步得到应用,但由于SMA的热力学效应,造成输入输出之间存在强饱和回滞非线性,从而影响了驱动性能.此外在引入负载后, SMA柔性驱动部件输出性能表现出更为复杂的驱动特性.因此,如何有效抑制带载条件下SMA柔性驱动部件强饱和非线性影响,成为提升驱动性能的关键.针对此问题,本文重点研究带载条件下SMA柔性驱动部件的建模及驱动控制算法.针对SMA驱动部件中的强饱和非线性特性,本文提出一类修正(MGPI)回滞模型来进行表征.通过设定线性输入形状函数,不仅有效解析表征SMA驱动部件中的饱和回滞非线性,并且便于控制器设计.基于MGPI模型,考虑柔性驱动部件的动态特性,本文提出了带载条件下的SMA柔性驱动部件的自适应神经网络控制算法,实现考虑内部非线性和外部干扰条件下的驱动精度有效提升,并保证全局稳定性.

关 键 词:形状记忆合金  回滞  自适应神经网络控制
收稿时间:2021/2/25 0:00:00
修稿时间:2021/7/1 0:00:00

Adaptive neural network control for SMA actuating flexible system based on MGPI hysteresis model
FENG Ying and LIANG Ming-wei.Adaptive neural network control for SMA actuating flexible system based on MGPI hysteresis model[J].Control Theory & Applications,2022,39(4):721-729.
Authors:FENG Ying and LIANG Ming-wei
Affiliation:South China University of Technology,South China University of Technology
Abstract:As a class of smart materials to be utilized as artificial muscles, SMAs have gradually been applied in the robotics and advanced manufacturing areas. However, due to the thermomechanical effects in SMA materials, the strong saturated hysteresis nonlinearities existing in the input-output relationship of SMA driving components will degrade the actuating accuracy. Besides, the output behavior of the SMA driving components is also affected by changes in the loads, presenting the more complex actuating performance. Therefore, the key to improve the actuating performance is to restrain the negative effects originating from the strong hysteresis nonlinearities with loads. Addressing this challenge, the modeling and actuating control algorithms for the SMA driving components under the loading conditions are discussed in this paper. For the strong saturated hysteresis property, a class of modified generalized Prandtl-Ishlinskii (MGPI) hysteresis model is proposed to describe this special feature. By setting the linear input shape function, the proposed MGPI hysteresis model can represent analytically the saturated hysteresis feature accurately and show the facilitation for the controller design. Based on the MGPI hysteresis model, considering the dynamics of the SMA actuators, an adaptive neural network control algorithm is discussed in this paper for the SMA driving components with loads. As an effective solution to problems of internal nonlinearity and external disturbance, the global stability of the closed-loop systems is ensured, and the actuating performance is guaranteed by the proposed method.
Keywords:shape memory alloy  hysteresis  adaptive NN control
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