SMA驱动器的反步自适应NN控制  

Back Stepping Adaptive Neural Network Control of Shape Memory Alloy Actuator

作　　者：姚茂鑫 李康 YAO Maoxin;LI Kang(College of Electrical Engineering,West China Hospital,Sichuan University,Chengdu 610041,China;West China Biomedical Big Data Center,West China Hospital,Sichuan University,Chengdu 610041,China)

机构地区：[1]四川大学电气工程学院,成都610041 [2]四川大学华西医院生物医学大数据中心,成都610041

出　　处：《组合机床与自动化加工技术》2023年第9期97-101,共5页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(51805449,62103291);四川省科技计划项目(2022YFS0021,2022YFH0073);四川大学华西医院1·3·5卓越学科项目(ZYYC21004,ZYJC21081)。

摘　　要：针对形状记忆合金(shape memory alloy, SMA)迟滞非线性特性下的轨迹跟踪问题,提出了一种反步自适应神经网络的控制方法。首先,根据所建立的Lyapunov函数,通过反步法设计出针对SMA驱动器的非线性控制器;然后,针对系统的非线性和参数的不确定性,采用自适应神经网络进行逼近,并且基于构造的Lyapunov函数,证明了闭环系统的稳定性。实验结果来看,所提出的控制方法成功地应用于SMA驱动器,并且几乎补偿了迟滞现象,并将实验控制结果与传统的PID控制器进行了比较。For the trajectory tracking problem under the hysteresis nonlinear characteristics of shape memory alloys,a backstepping adaptive neural network control method was proposed.Firstly,based on the established Lyapunov function,a nonlinear controller for the SMA actuator is designed by a backstepping method.Then,adaptive neural networks are used to approximate the nonlinearity of the system and the uncertainty of the parameters,and the stability of the closed-loop system is proved based on the constructed Lyapunov function.Experimental results show that compared with the traditional PID controller,the proposed control method is successfully applied to the SMA actuator and almost compensates for the hysteresis.

关 键 词：形状记忆合金 驱动器 自适应神经网络 反步控制 迟滞非线性 

分 类 号：TH164[机械工程—机械制造及自动化] TG68[金属学及工艺—金属切削加工及机床]