Neural network modeling and generalized predictive control for Giant Magnetostrictive actuators

In the application of the giant magnetostrictive actuators (GMA), hysteresis of the GMA is particularly significant and causes undesired effect in the control system. This paper investigates the application of neural network based on generalized predictive control to eliminate the hysteresic effect...

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Bibliographic Details
Main Authors: ShiFeng Hu, ShiJian Zhu, MinJun Zhong, QiWei He
Format: Conference Proceeding
Language:English
Subjects:
Actuators
Feedforward neural networks
Giant Magnetostrictive Actuators
Hysteresis
Inverse problems
Magnetization
Magnetostriction
MENN
multi-step Predictive Control
Neural network
Neural networks
Predictive control
Predictive models
Proportional control
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Summary:In the application of the giant magnetostrictive actuators (GMA), hysteresis of the GMA is particularly significant and causes undesired effect in the control system. This paper investigates the application of neural network based on generalized predictive control to eliminate the hysteresic effect of GMA. The modified Elman neural network is used as the multi-step predictive model, the fused identification model is proposed to improve the predictive and control precision. The modified Elman neural network on-line learning improves the control system adaptability to the unpredicted operating environment for GMA. Simulations on GMA position control are included to illustrate the effectiveness of the proposed control scheme.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2009.5191921