面向非线性多智能体系统的仅状态信息与有限感知下可证安全的分散式应急模型预测控制
Provably Safe Decentralized Contingency MPC under State-Only Information and Limited Sensing for Nonlinear Multi-agent Systems
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中文总结 AI 辅助
针对仅状态信息与有限感知下的非线性多智能体系统，提出可证安全的分散式应急模型预测控制方法，通过新颖安全集更新机制降低保守性，在密集场景中验证了其有效性。
中文摘要 AI 辅助
本文研究仅采用状态信息模式的多智能体控制分散式应急模型预测控制（contingency MPC），重点关注有限感知与即插即用操作。目标是在处理局部交互时降低保守性，同时保持递归可行性、安全性与李雅普诺夫型收敛性。该框架依赖智能体专属的 fallback 区域（安全集），其中始终存在通向安全平衡点的可行应急机动。本文提出一种新颖的安全集更新机制，支持保守性更低的分散式交互，同时保留底层保证，进而实现无记忆的局部交互与有限感知范围，无需智能体重构精确的邻域几何结构。所得方案保持完全分散式结构，保留共享首输入应急模型预测控制架构。理论保证与仿真结果表明，该方法在密集多智能体场景中具有有效性。
英文摘要
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.
