Distributed Source Seeking With Global Field Exploration Using Adaptive Model Predictive Control

• 成果类型: Article
• 署名作者: Gao, Xinzhou; Shu, Zhan; Liu, Jason J. R.
• 署名单位: University of Alberta; University of Macau
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3666701
• 发表日期: 2026
• 关键词: Extremum seeking; STABILITY; MPC; algorithms; systems; set

摘要

In this article, we address the distributed source seeking (DSS) problem using model predictive control (MPC). In our approach, the signal field is modeled as a weighted combination of convex functions, and we employ a distributed set-membership estimation to gain a global exploration of the field. This estimated field is then treated as the output of the resulting connected system, enabling the DSS problem to be formulated as an output tracking problem that can be solved using a distributed adaptive MPC (DAMPC) approach. To ensure the recursive feasibility of DAMPC, we propose a parametrized terminal set design, derived from the characteristics of the signal field and system dynamics. We also demonstrate that the closed-loop system is practically stable. Simulation results validate the effectiveness of our method and highlight its superiority over existing DSS algorithms.