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Simulation Research for Giant Magnetostrictive Actuator Controller Using Model Reference Control Based on Neural Network

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2 Author(s)
Yang Lingxiao ; Henan Polytech. Univ., Jiaozuo, China ; Zhong Ying

Giant Magnetostrictive Material (GMM) has inherent hysteretic nonlinearity, and its hysteretic performance changes with input frequency. Hence, it is difficult for a normal controller to control its actuator precisely. Due to this, a hysteretic compensation control strategy was proposed. Adopting neural network model reference, combine the dynamic model of Giant Magnetostrictive Actuator (GMA) as reference model, with BP neural network. Introducing error feed-back learning scheme - BP into controller and identifier, controller can identify GMA and identifier control it precisely. To accelerate the convergence of the trace error, train the neural network offline.

Published in:

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on  (Volume:3 )

Date of Conference:

11-12 May 2010