Event-Triggered-Based Neuroadaptive Bipartite Containment Tracking for Networked Unmanned Aerial Vehicles

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