Problem
Research questions and friction points this paper is trying to address.
decentralized contingency MPC
state-only information
limited sensing
nonlinear multi-agent systems
recursive feasibility
Provably Safe Decentralized Contingency MPC under State-Only Information and Limited Sensing for Nonlinear Multi-agent Systems
🤖 AI Summary
本文提出了一种在仅有状态信息和有限感知下的多智能体系统的分散式应急MPC方法，通过引入新的安全集更新机制来减少保守性并保持安全性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
decentralized contingency MPC
state-only information
limited sensing
safe-set update mechanism
multi-agent systems
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