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• W2312888267 abstract "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 no n-minimum output responses in particular under persistent excitatio n. 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 a ctive vibration control of smart structures. The neural controller is a fund amental 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 demon strate the effectiveness of the proposed control method." @default.
• W2312888267 created "2016-06-24" @default.
• W2312888267 creator A5042750801 @default.
• W2312888267 creator A5084478196 @default.
• W2312888267 date "2015-02-01" @default.
• W2312888267 modified "2023-09-27" @default.
• W2312888267 title "Improving Neural Network based Vibration Control for Smart Structures by Adding Repetitive Control" @default.
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• W2312888267 doi "https://doi.org/10.12785/amis/091l15" @default.
• W2312888267 hasPublicationYear "2015" @default.
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