Real-Time Distributed Model Predictive Control With Limited Communication Data Rates

• 成果类型: Article
• 署名作者: Yang, Yujia; Wang, Ye; Manzie, Chris; Pu, Ye
• 署名单位: University of Melbourne; University of Melbourne
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2025.3545220
• 发表日期: 2025
• 页码: 4928-4935
• 关键词: Quantization (signal); optimization; Stability criteria; Real-time systems; nickel; Heuristic algorithms; Distributed databases; noise; Predictive control; cost function; Distributed model predictive controller (DMPC); distributed optimization; limited communication data rate; QUANTIZATION

摘要

The application of distributed model predictive controllers (DMPC) for multiagent systems (MASs) necessitates communication between agents, yet the consequence of communication data rates is typically overlooked. This work focuses on developing stability-guaranteed control methods for MASs with limited data rates. Initially, a distributed optimization algorithm with dynamic quantization is considered for solving the DMPC problem. Due to the limited data rate, the optimization process suffers from inexact iterations caused by quantization noise and premature termination, leading to suboptimal solutions. In response, we propose a novel real-time DMPC framework with a quantization refinement scheme that updates the quantization parameters on-line so that both the quantization noise and the optimization suboptimality decrease asymptotically. To facilitate the stability analysis, we treat the suboptimally controlled MAS, the quantization refinement scheme, and the optimization process as three interconnected subsystems. The cyclic-small-gain theorem is used to derive sufficient conditions on the quantization parameters for guaranteeing the stability of the system under a limited data rate. Finally, the proposed algorithm and theoretical findings are demonstrated in a multi-AUV formation control example.