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Adaptive extreme learning machine‐based event‐triggered control for perturbed Euler–Lagrange systems

Jin, X‐Z, Gao, M‐M, Che, W‐W and Wang, H.ORCID: 0000-0003-2789-9530 (2022) Adaptive extreme learning machine‐based event‐triggered control for perturbed Euler–Lagrange systems. International Journal of Robust and Nonlinear Control . Early View.

Link to Published Version: https://doi.org/10.1002/rnc.6377
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Abstract

The problem of event-triggered finite-time trajectory tracking control of perturbed Euler–Lagrange systems with nonlinear dynamics and disturbances is addressed in this article. Extreme learning machine (ELM) framework is employed to formulate unknown nonlinearities, and adaptive technique is adopted to adjust output weights of the ELM networks and remedy the negative impacts of disturbances, nonlinearities, and residual errors. Then to ensure the system follows the desired position trajectory within a finite-time, an adaptive ELM-based sliding mode control strategy is developed. Moreover, event-triggered control technique is proposed to regulate control outputs on the basis of the developed control strategy for reducing actuator actions and saving communication resources. Lyapunov stability theorem is utilized to confirm bounded trajectory tracking results and finite-time convergence of the Euler–Lagrange system. Finally, the effectiveness of the developed adaptive ELM-based event-triggered sliding-mode control strategies is substantiated by simulations in a robotic manipulator system.

Item Type: Journal Article
Murdoch Affiliation(s): Engineering and Energy
Publisher: John Wiley & Sons, Ltd.
Copyright: © 2022 John Wiley & Sons Ltd.
URI: http://researchrepository.murdoch.edu.au/id/eprint/66215
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