2024/10/25 by Haojin Li, Xiaodong Cheng, Li, Haojin +5
Computer Science · Decision Sciences · Engineering · #Advanced Optical Network Technologies #FOS: Mathematics #Network Time Synchronization Technologies #Optimization and Control (math.OC) #Simulation Techniques and Applications
paper · pdf · doi:10.48550/arxiv.2410.19458
openalex publication_date 2024/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we present an event-triggered distributed optimization approach including a distributed controller to solve a class of distributed time-varying optimization problems (DTOP). The proposed approach is developed within a distributed neurodynamic (DND) framework that not only optimizes the global objective function in real-time, but also ensures that the states of the agents converge to consensus. This work stands out from existing methods in two key aspects. First, the distributed controller enables the agents to communicate only at designed instants rather than continuously by an event-triggered scheme, which reduces the energy required for agent communication. Second, by incorporating an integral mode technique, the event-triggered distributed controller avoids computing the inverse of the Hessian of each local objective function, thereby reducing computational costs. Finally, an example of battery charging problem is provided to demonstrate the effectiveness of the proposed event-triggered distributed optimization approach.