Event-triggered control for distributed time-varying optimization

In this paper, we propose a novel event-triggered (ET) distributed neurodynamic (DND) approach that integrates a distributed controller to tackle distributed time-varying optimization problems (DTOP). The approach achieves optimization of a global cost function in real time while simultaneously steering agent states toward consensus. Two key features distinguish the proposed method from prior works. First, communication among agents is governed by ET schemes, allowing updates only at specific triggering moments, which helps conserve communication energy. Second, the ET distributed controller eliminates the computation of the inverse of the Hessian matrix of the local objective function, which effectively reduces the computational cost. Finally, a case study of the battery charging problem demonstrates the effectiveness of the proposed approach.