Privacy-Protected and Prescribed-Time Dynamic Average Consensus Over Directed Networks: An Integral Surplus-Based Approach

• 成果类型: Article
• 署名作者: Cao, Runhua; Zhao, Yu; Liu, Yongfang; Huang, Panfeng
• 署名单位: Northwestern Polytechnical University; Northwestern Polytechnical University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3578246
• 发表日期: 2025
• 关键词: REFERENCE SIGNALS; tracking; algorithms

摘要

This article addresses the design problem of the privacy-protected and prescribed-time dynamic average consensus (DAC) algorithm over possibly unbalanced directed networks-the most general and most challenging case from the perspective of typologies, which has been rarely studied in existing literature. First, by developing an integral surplus-based prescribed-time control framework, a prescribed-time DAC algorithm is designed over unbalanced directed networks, which allows all agents to achieve DAC with minimal steady-state error in a prescribed-settling time. Compared with existing works on DAC, it is the first time to solve the prescribed-time problem over directed networks. Second, a privacy attack model is developed in this article, which may help an external eavesdropper easily wiretap the privacy-sensitive data in the existing DAC algorithms. To avoid the privacy data leakage, a state decomposition scheme is embedded in the proposed prescribed-time DAC algorithm with privacy-protected requirements. With the help of privacy-protected DAC algorithms, the previous privacy attack model cannot wiretap the privacy-sensitive data of agents anymore. Finally, some simulation examples display the validity of the proposed privacy-protected and prescribed-time DAC algorithms.