Dynamic Average Consensus Over Strongly Connected Digraphs Based on Integral Surplus

• 成果类型: Article
• 署名作者: Cao, Runhua; Liu, Yongfang; Zhao, Yu; Wu, Dapeng Oliver; Chen, Guanrong
• 署名单位: Northwestern Polytechnical University; City University of Hong Kong; City University of Hong Kong
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3584699
• 发表日期: 2025
• 关键词: REFERENCE SIGNALS; tracking

摘要

This article addresses the design of a dynamic average consensus (DAC) algorithm over strongly connected but may not necessarily balanced digraphs, which seems to be the first time in the literature. Specifically, a new concept of integral surplus is proposed for DAC problems. On this basis, an integral surplus DAC algorithm is developed for agents to track the average of their multiple dynamic input signals with a bounded steady-state error. Such error is tunable by some algorithm parameters and even vanishes for special classes of input signals. Simulation examples are presented to verify the theoretical results.