Fuzzy Adaptive Constrained Consensus Tracking of High-Order Multi-agent Networks: A New Event-Triggered Mechanism

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 9; pp. 5468 - 5480
• Main Authors: Wang, Ning, Wang, Ying, Wen, Guanghui, Lv, Maolong, Zhang, Fan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backstepping; Constraints; Convergence; Design parameters; Event-triggered control (ETC); full-state constraints; high-order multiagent networks; Laplace equations; Liapunov functions; Lyapunov methods; Multiagent systems; Navigation; Task analysis; Tracking errors; Upper bound
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2021.3127825

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.