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

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