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Improving Neural Network based Vibration Control for Smart Structures by Adding Repetitive Control |
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PP: 117-124 |
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Author(s) |
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Chi-Ying Lin,
Chih-Ming Chang,
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Abstract |
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| Neural networks (NN) has been a popular vibration control method because of its robustness and practicability to reject
broad band disturbances for complex systems such as smart structures. However, the benign characteristic of NN, suppressing a wide
range frequency of disturbances, may also limit its control performance at specific frequencies and inevitably cause non-minimum
output responses in particular under persistent excitation. To alleviate this limitation and improve the performance of NN based control
methods, this paper presents a hybrid control strategy comprising a neural controller and a repetitive controller for active vibration
control of smart structures. The neural controller is a fundamental controller which applies back-propagation networks for performance
evaluation. To add repetitive control into the existing NN control system, the work transforms a feedback controller to a feedforward
control problem with the solutions of a bezout identity embedded with known internal models of injecting disturbances. The presented
hybrid control provides a synergetic effect and aims for better suppression performance subject to complicated disturbances in stringent
environments. Experimental results on a flexible beam demonstrate the effectiveness of the proposed control method. |
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