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Neural network-based self-learning control for power transmission line deicing robot

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Abstract

Recently, the application of the maintenance transmission line robot has been very popular in the power industry. However, difficulties in the control of maintenance transmission line robot exist due to multiple nonlinearities, plant parameter variations and external disturbances. This paper investigates the possibility of using neural network as a promising self-learning control alternative for the control problem of inspection and deicing transmission line robot. We first discuss the mechanical structure, as well as dynamic model of a deicing robot. And then, a neural network-based self-learning control strategy consists of a fuzzy neural network controller and an ELM-based single-layer-feedback neural networks identifier are proposed for this deicing transmission line robot. Both the structure and the learning algorithm of the control system are presented. The proposed controller is verified by computer simulations and experiments.

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Acknowledgments

This work was supported by National Technology Support Project (2008BAF36B01), National Natural Science Foundation of China (60835004, 61175075, 61104088) and National High Technology Research and Development Program of China (863 Program: 2008AA04Z214). The authors would like to thank the editor and anonymous reviewers for their invaluable suggestions, which has been incorporated to improve the quality of this paper dramatically.

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  1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, China

    Yimin Yang, Yaonan Wang, Xiaofang Yuan, Youhui Chen & Lei Tan

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  1. Yimin Yang
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  2. Yaonan Wang
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  3. Xiaofang Yuan
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  4. Youhui Chen
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  5. Lei Tan
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Correspondence to Yimin Yang.

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Yang, Y., Wang, Y., Yuan, X. et al. Neural network-based self-learning control for power transmission line deicing robot. Neural Comput & Applic 22, 969–986 (2013). https://doi.org/10.1007/s00521-011-0789-x

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