Neural adaptive predefined-time formation tracking control of multiple Euler–Lagrange systems with switching topologies based on hierarchical mechanism

Xiao-Wen Zhao, Dong-Dong Deng, Ming-Feng Ge and Zhi-Wei Liu

Chaos, Solitons & Fractals, 2024, vol. 178, issue C

Abstract: This paper investigates the event-triggered neural adaptive predefined-time time-varying formation tracking control (PTTVFTC) problem for multiple Euler–Lagrange systems (ELSs) with actuator failures and saturation under switching communication topologies. The hierarchical control mechanism (HCM) is employed to overcome the difficulties posed by switching communication topologies to design the control algorithms. Specifically, a new estimator is first developed in the estimator layer for each follower, which can estimate the leader’s position information within a predefined time. Then, based on the designed estimator, a fault-tolerant and anti-saturation controller is proposed in the local control layer for each follower, which enables each follower to achieve PTTVFTC. To reduce the communication frequency, an event-triggered mechanism (ETM) that does not have Zeno behavior determines the controller’s update. In addition, the settling time for formation tracking can be arbitrarily specified as desired. Finally, the effectiveness of the developed control strategy is verified by a simulation experiment.

Keywords: Predefined-time time-varying formation tracking control (PTTVFTC); Multiple Euler–Lagrange systems (ELSs); Switching topologies; Neural adaptive fault-tolerant control; Hierarchical control mechanism (HCM) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077923012778
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:178:y:2024:i:c:s0960077923012778

DOI: 10.1016/j.chaos.2023.114375

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().