Volume 12 Number 3
June 2015
Article Contents
Yu Liu, Rong-Hu Chi and Zhong-Sheng Hou. Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points. International Journal of Automation and Computing, vol. 12, no. 3, pp. 266-272, 2015 doi:  10.1007/s11633-015-0891-0
Cite as: Yu Liu, Rong-Hu Chi and Zhong-Sheng Hou. Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points. International Journal of Automation and Computing, vol. 12, no. 3, pp. 266-272, 2015 doi:  10.1007/s11633-015-0891-0

Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points

  • Received: 2014-05-26
Fund Project:

This work was supported by National Natural Science Foundation of China (Nos. 61374102, 61433002 and 61120106009) and High Education Science & Technology Fund Planning Project of Shandong Province of China (No. J14LN30).

通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points

Fund Project:

This work was supported by National Natural Science Foundation of China (Nos. 61374102, 61433002 and 61120106009) and High Education Science & Technology Fund Planning Project of Shandong Province of China (No. J14LN30).

Abstract: Terminal iterative learning control (TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis. A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.

Yu Liu, Rong-Hu Chi and Zhong-Sheng Hou. Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points. International Journal of Automation and Computing, vol. 12, no. 3, pp. 266-272, 2015 doi:  10.1007/s11633-015-0891-0
Citation: Yu Liu, Rong-Hu Chi and Zhong-Sheng Hou. Neural Network State Learning Based Adaptive Terminal ILC for Tracking Iteration-varying Target Points. International Journal of Automation and Computing, vol. 12, no. 3, pp. 266-272, 2015 doi:  10.1007/s11633-015-0891-0
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