Event-Triggered Distributed Aggregative Optimization of Heterogeneous High-Order Systems

This article investigates the event-triggered distributed aggregative optimization problem for heterogeneous higher-order integrator systems. A novel distributed event-triggered algorithm ETDAO is proposed that integrates the dynamic average consensus technique with the gradient descent method to efficiently solve the aggregative optimization problem. Under the event-triggered scheme, continuous communication between agents is avoided. The exponential convergence of the algorithm is established through rigorous stability analysis. Finally, the efficiency of the algorithm is verified by numerical simulations.