Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles DOI Creative Commons
Bowen Chen, Boxian Lin,

Meng Li

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(4), P. 317 - 317

Published: April 21, 2025

This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed mechanism is innovatively developed eliminate dependency on global information while rigorously excluding Zeno phenomenon through nonperiodic threshold verification. The proposed enables neighboring UAVs exchange update control signals exclusively at triggering instants, significantly reducing communication burdens energy consumption. To handle unknown nonlinear dynamics under resource-limited scenarios, a novel neural network (NN) approximation scheme established where weight updating occurs only during event triggers, effectively decreasing computational occupation. Simultaneously, an adaptive robust compensation constructed counteract composite disturbances induced by failures residuals. Based Lyapunov stability analysis, we theoretically prove that all closed-loop remain uniformly ultimately bounded achieving prescribed objectives, follower converge dynamic convex hull formed multiple leaders with cooperative-competitive interactions. Finally, numerical simulations are conducted validate effectiveness of theoretical results. Comparative simulation results show reduces utilization resources 95% 67% compared traditional time-triggered static-triggered mechanisms, respectively.

Language: Английский

Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles DOI Creative Commons
Bowen Chen, Boxian Lin,

Meng Li

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(4), P. 317 - 317

Published: April 21, 2025

This paper addresses the event-triggered neuroadaptive bipartite containment tracking problem for networked unmanned aerial vehicles (UAVs) subject to resource constraints and actuator failures. A fully distributed mechanism is innovatively developed eliminate dependency on global information while rigorously excluding Zeno phenomenon through nonperiodic threshold verification. The proposed enables neighboring UAVs exchange update control signals exclusively at triggering instants, significantly reducing communication burdens energy consumption. To handle unknown nonlinear dynamics under resource-limited scenarios, a novel neural network (NN) approximation scheme established where weight updating occurs only during event triggers, effectively decreasing computational occupation. Simultaneously, an adaptive robust compensation constructed counteract composite disturbances induced by failures residuals. Based Lyapunov stability analysis, we theoretically prove that all closed-loop remain uniformly ultimately bounded achieving prescribed objectives, follower converge dynamic convex hull formed multiple leaders with cooperative-competitive interactions. Finally, numerical simulations are conducted validate effectiveness of theoretical results. Comparative simulation results show reduces utilization resources 95% 67% compared traditional time-triggered static-triggered mechanisms, respectively.

Language: Английский

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