An integrated dynamic event-driven ADP framework for distributed resource allocation in nonlinear multi-agent systems

This paper investigates the distributed resource allocation problem in second-order nonlinear multi-agent systems (MASs) and proposes an integrated adaptive dynamic programming (ADP) framework with a dynamic event-triggered mechanism. A coupled performance index based on resource allocation errors is first formulated, embedding a positive-definite quadratic form to transform the resource allocation control problem into the design of optimal control policies for all agents. Within this framework, a fixed-time single-critic learning law is developed to approximate the Hamilton-Jacobi-Bellman (HJB) equation online. The dynamic event-triggered mechanism updates the control input only when the adaptive triggering condition is violated. Theoretical analysis guarantees the uniform ultimate boundedness of all closed-loop signals. Numerical simulations verify the effectiveness of the proposed framework in achieving accurate resource allocation while balancing allocation errors and control efforts.