Neural network-based robust consensus tracking for uncertain networked Euler-Lagrange systems with time-varying delays and output constraints

Runlong Peng, Rongwei Guo, Bin Zheng, Zhonghua Miao and Jin Zhou

Applied Mathematics and Computation, 2024, vol. 468, issue C

Abstract: This paper mainly focuses on the cooperative robust consensus tracking problem of uncertain networked Euler-Lagrange systems (NELSs) with time-varying delays and output constraints. By systematically integrating the neural network (NN) adaptive technique and the logarithmic type Barrier Lyapunov Function (BLF) in combination with the additional robust control law, two distributed robust consensus schemes for uncertain NELSs are proposed for two cases of time-varying communication and input delays respectively, which can fully guarantee to constrain the output consensus error within a safety region simultaneously. Furthermore, numerical simulation examples are provided to demonstrate the comparable potential advantages of the proposed robust control law over some existing algorithms, including adaptability, stability, and robustness, as well as delay effects.

Keywords: Networked Euler-Lagrange systems (NELSs); Neural network (NN); Time-varying delays; Consensus tracking; Output constraints (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:468:y:2024:i:c:s0096300323006914

DOI: 10.1016/j.amc.2023.128522

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