Leaderless Consensus of Heterogeneous Multiple Euler-Lagrange Systems With Unknown Disturbance

Leaderless Consensus of Heterogeneous Multiple Euler-Lagrange Systems With Unknown Disturbance

Wang, Shimin (Queen’s University)
Zhang, Hongwei (Harbin Institute of Technology)
Baldi, S. (TU Delft Team Bart De Schutter; Southeast University)
Zhong, Renxin (Sun Yat-sen University)

2023

This article studies the leaderless consensus problem of heterogeneous multiple networked Euler-Lagrange systems subject to persistent disturbances with unknown constant biases, amplitudes, initial phases, and frequencies. The main characteristic of this study is that none of the agents has information of a common reference model or of a common reference trajectory. Therefore, the agents must simultaneously and in a distributed way: achieve consensus to a common reference model (group model); achieve consensus to a common reference trajectory; and reject the unknown disturbances. We show that this is possible via a suitable combination of techniques of distributed 'observers,' internal model principle and adaptive regulation. The proposed design generalizes recent results on group model learning, which have been studied for linear agents over undirected networks. In this article, group model learning is achieved for Euler-Lagrange dynamics over directed networks in the presence of persistent unknown disturbances.

Cooperative control
Euler-Lagrange system
leaderless consensus
multiagent system
output regulation

http://resolver.tudelft.nl/uuid:138bc2a5-6273-4c1e-b3f4-d326dfc559a3

https://doi.org/10.1109/TAC.2022.3172594

2023-11-05

0018-9286

IEEE Transactions on Automatic Control, 68 (4), 2399-2406

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Shimin Wang, Hongwei Zhang, S. Baldi, Renxin Zhong