Modeling and Control for Giant Magnetostrictive Actuators with Rate-Dependent Hysteresis

被引:23
|
作者
Liu, Ping [1 ]
Zhang, Zhen [1 ]
Mao, Jianqin [1 ]
机构
[1] Beijing Univ Aeronaut & Astronaut, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
关键词
NEURAL-NETWORKS; IDENTIFICATION; COMPENSATION;
D O I
10.1155/2013/427213
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The rate-dependent hysteresis in giant magnetostrictive materials is a major impediment to the application of such material in actuators. In this paper, a relevance vector machine (RVM) model is proposed for describing the hysteresis nonlinearity under varying input current. It is possible to construct a unique dynamic model in a given rate range for a rate-dependent hysteresis system using the sinusoidal scanning signals as the training set input signal. Subsequently, a proportional integral derivative (PID) control scheme combined with a feedforward compensation is implemented on a giant magnetostrictive actuator (GMA) for real-time precise trajectory tracking. Simulations and experiments both verify the effectiveness and the practicality of the proposed modeling and control methods.
引用
收藏
页数:8
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