提出安全集更新机制，实现多智能体系统在有限感知下的安全去中心化控制。
Provably Safe Decentralized Contingency MPC under State-Only Information and Limited Sensing for Nonlinear Multi-agent Systems
- 基于安全集的去中心化交互设计，降低局部约束保守性。
- 支持无记忆本地交互与有限感知范围，无需重构邻居几何结构。
- 适用于密集多智能体场景，保障递归可行性与收敛性，适合实际部署。
本文研究在仅状态信息、有限感知和即插即用操作条件下的多智能体系统去中心化应急模型预测控制（MPC）。目标是在保持递归可行性、安全性及李雅普诺夫型收敛性的同时，降低局部交互的保守性。框架依赖于各智能体的备用安全区域（安全集），确保可随时执行到安全平衡点的应急动作。提出一种新型安全集更新机制，支持更宽松的去中心化交互，同时保留原有理论保证。该机制实现无记忆本地交互与有限感知范围，无需重构邻近智能体几何结构。所提方法保持完全去中心化，且维持共享首输入的应急MPC结构。理论分析与仿真结果验证了该方法在高密度多智能体场景中的有效性。
原文摘要 · Abstract (English)
This paper considers decentralized contingency MPC for multi-agent control under a state-only information pattern, with particular focus on limited sensing and plug-and-play operation. The objective is to retain recursive feasibility, safety, and Lyapunov-type convergence while reducing conservatism in local interaction handling. The framework relies on agent-wise fallback regions (safe sets) in which a feasible contingency maneuver to a safe equilibrium is always available. A novel safe-set update mechanism is introduced that supports less conservative decentralized interaction while preserving the underlying guarantees. This, in turn, enables memory-free local interaction and finite sensing ranges without requiring agents to reconstruct the exact neighbor geometry. The resulting scheme remains fully decentralized and preserves the shared-first-input contingency MPC structure. Theoretical guarantees and simulation results illustrate the effectiveness of the approach in dense multi-agent scenarios.
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