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Neural Computing and Applications

Erschienen in:

01.04.2013 | Original Article

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

verfasst von: Yimin Yang, Yaonan Wang, Xiaofang Yuan, Youhui Chen, Lei Tan

Erschienen in: Neural Computing and Applications | Ausgabe 5/2013

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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.

Vorheriger Artikel Applications of an artificial intelligence for servicing of a technical object

Nächster Artikel Adaptive neuro-fuzzy inference system–based model for elevation–surface area–storage interrelationships

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Titel: Neural network-based self-learning control for power transmission line deicing robot
verfasst von: Yimin Yang
Yaonan Wang
Xiaofang Yuan
Youhui Chen
Lei Tan
Publikationsdatum: 01.04.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 5/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0789-x

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