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Online sequential fuzzy dropout extreme learning machine compensate for sliding-mode control system errors of uncertain robot manipulator

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Abstract

An online sequential fuzzy dropout scheme is proposed to track the position of robot manipulators in this paper. The scheme is based on the extreme learning machine–inherited sliding-mode control (OSFDELMISMC). In this scheme, an improved extreme learning machine, called the online sequential fuzzy dropout extreme learning machine (OSFDELM), is utilized to mimic the control law of sliding-mode, update the network parameters through online cyclic training, and relax the detailed system information using the fuzzy method. To ensure network convergence and stable control performance, this paper obtains the network adaptive learning law through the Lyapunov stability theorem. The simulation results indicate that the OSFDELMISMC scheme is a feasible control scheme under which the trajectories of the two-link robot manipulator are accurately tracked, and chattering is effectively reduced.

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Acknowledgements

This work is supported by Key R&D Program of Zhejiang Province (No. 2021C03013).

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Authors and Affiliations

  1. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, China

    Zhiyu Zhou & Haodong Ji

  2. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Zefei Zhu

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  1. Zhiyu Zhou
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  2. Haodong Ji
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  3. Zefei Zhu
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Correspondence to Zhiyu Zhou.

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Zhou, Z., Ji, H. & Zhu, Z. Online sequential fuzzy dropout extreme learning machine compensate for sliding-mode control system errors of uncertain robot manipulator. Int. J. Mach. Learn. & Cyber. 13, 2171–2187 (2022). https://doi.org/10.1007/s13042-022-01513-x

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  • DOI: https://doi.org/10.1007/s13042-022-01513-x

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