Fuzzy Adaptive Constrained Consensus Tracking of High-Order Multi-agent Networks

A New Event-Triggered Mechanism

Journal Article (2022)
Author(s)

Ning Wang (Air Force Engineering University China)

Ying Wang (Air Force Engineering University China)

Guanghui Wen (Southeast University)

Maolong Lv (TU Delft - Team Bart De Schutter, Air Force Engineering University China)

Fan Zhang (Sun Yat-sen University)

Research Group
Team Bart De Schutter
Copyright
© 2022 Ning Wang, Ying Wang, Guanghui Wen, Maolong Lv, Fan Zhang
DOI related publication
https://doi.org/10.1109/TSMC.2021.3127825
More Info
expand_more
Publication Year
2022
Language
English
Copyright
© 2022 Ning Wang, Ying Wang, Guanghui Wen, Maolong Lv, Fan Zhang
Research Group
Team Bart De Schutter
Issue number
9
Volume number
52
Pages (from-to)
5468-5480
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

This article aims to realize event-triggered constrained consensus tracking for high-order nonlinear multiagent networks subject to full-state constraints. The main challenge of achieving such goals lies in the fact that the standard designs [e.g., backstepping, event-triggered control, and barrier Lyapunov functions (BLFs)] successfully developed for low-order dynamics fail to work for high-order dynamics. To tackle these issues, a novel high-order event-triggered mechanism is devised to update the actual control input, lowering the communication and computation burden. More precisely, compared with the conventional event-triggered mechanism, not only the amplitudes of control signals and a fixed threshold are considered but a monotonically decreasing function is introduced to allow a relatively big threshold, while guaranteeing consensus tracking error to be small. Then, a high-order tan-type BLF working for both constrained and unconstrained scenarios is incorporated into the distributed adding-one-power-integrator design for the purpose of confining full states within some compact sets all the time. A finite-time convergent differentiator (FTCD) is introduced to circumvent the ``explosion of complexity.'' The consensus tracking error is shown to eventually converge to a residual set whose size can be adjusted as small as desired through choosing appropriate design parameters. Comparative simulations have been conducted to highlight the superiorities of the developed scheme.

Files

Fuzzy_Adaptive_Constrained_Con... (pdf)
(pdf | 1.92 Mb)
- Embargo expired in 01-07-2023
License info not available