Haptic identification by ELM-controlled uncertain manipulator

Yang, Chenguang, Huang, Kunxia, Cheng, Hong, Li, Yanan and Su, Chun-Yi (2017) Haptic identification by ELM-controlled uncertain manipulator. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (8). pp. 2398-2409. ISSN 2168-2216

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Abstract

This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies. Index Terms—Extreme learning machine; haptic identification; adaptive control; robot manipulator.

Item Type: Article
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Schools and Departments: School of Engineering and Informatics > Engineering and Design
Depositing User: Yanan Li
Date Deposited: 14 Dec 2017 16:34
Last Modified: 14 Dec 2017 16:34
URI: http://sro.sussex.ac.uk/id/eprint/72064

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