Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 47, Issue 8, Pages 2398-2409Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2676022
Keywords
Adaptive control; extreme learning machine (ELM); haptic identification; robot manipulator
Funding
- National Nature Science Foundation [61473120, U1613223]
- Guangdong Provincial Natural Science Foundation [2014A030313266]
- Guangdong Provincial International Science and Technology Collaboration [2015A050502017]
- Science and Technology Planning Project of Guangzhou [201607010006]
- State Key Laboratory of Robotics and System (HIT) [SKLRS-2017-KF-13]
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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.
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