*Result*: Resource allocation over nonlinear uncertain multi-agent systems: An efficient communication perspective.

*Multi-agent system (MAS), as the main framework for distributed resource allocation, may be subject to resource limitations, communication bandwidth constraints, and environmental changes in actual operation, which can lead to nonlinear behavior, uncertain parameters, and unbalanced communication in the MAS. To address these challenges, this paper proposes a novel distributed algorithm that includes a controller and an optimal estimator. By incorporating a dynamic compensation mechanism and tracking technique, the proposed algorithm effectively handles system nonlinearities, tracks uncertain parameters, and compensates for asymmetric communication, thereby enhancing the system's robustness and expanding its application scope. Furthermore, to further improve the communication efficiency of MAS, a dynamic event-triggered mechanism is integrated with the dynamic compensation mechanism, ensuring that MAS can effectively conserve communication resources under unbalanced communication topologies. Finally, the proposed algorithm is validated through a case study involving a five-drone cooperative formation in a nonlinear uncertain MAS. • Coupled-constrained nonlinear uncertain MAS: Extends resource allocation to nonlinear uncertain MASs with coupled equality constraints. • Unbalanced directed topology: A compensation-based distributed algorithm ensures robustness under asymmetric information flow. • Dynamic event-triggered mechanism: A three-variable strategy reduces communication load while guaranteeing convergence. • Validated on multi-drone formation: A five-drone case verifies effectiveness in nonlinear uncertain MASs. [ABSTRACT FROM AUTHOR]*