Scenario MPC with STL Specifications and Pareto-Based Feasibility Repair

2026-09-19

AI Summary

本文针对多智能体系统中存在随机性和不可控性的问题，提出了一种基于Pareto优化的模型预测控制框架，并通过概率证书量化不确定性。

Abstract

Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode spatio-temporal requirements for control synthesis in multi-agent systems, often under the assumption that agents are cooperative and their dynamics are known. However, real-world multi-agent applications, such as autonomous driving, typically involve stochastic and uncontrollable agents. Recent work explored robust control with worst-case or probabilistic formulations, but remains limited in that it either (1) certifies strict satisfaction of STL constraints without addressing feasibility recovery, or (2) relaxes infeasible constraints with ego-centric objectives. In this paper, we propose a model predictive control (MPC) framework that treats feasibility repair as a Pareto optimization problem to explicitly characterize tradeoffs among agent objectives. We further provide a probabilistic certificate on STL violation rate to formally quantify uncertainty under stochastic and uncontrollable agents. The proposed framework is evaluated on two autonomous driving scenarios. Results show that the framework recovers feasible control with demonstrated safe behaviors.

Problem

• Temporal Logic
• Multi-Agent Systems
• Feasibility Repair
• Stochastic Agents
• Pareto Optimization

Innovation

• Pareto optimization
• feasibility repair
• probabilistic certificate
• STL violation rate
• model predictive control (MPC)