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Haptic identification by ELM-controlled uncertain manipulator
journal contribution
posted on 2023-06-09, 09:22 authored by Chenguang Yang, Kunxia Huang, Hong Cheng, Yanan LiYanan Li, Chun-Yi SuThis 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.
History
Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on Systems, Man, and Cybernetics: SystemsISSN
2168-2216Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Issue
8Volume
47Page range
2398-2409Department affiliated with
- Engineering and Design Publications
Notes
(c)2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksFull text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-12-14First Open Access (FOA) Date
2017-12-14First Compliant Deposit (FCD) Date
2017-12-14Usage metrics
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