CAREER: A Partial Order Approach to Dynamic Feedback in Multi-agent Decision and Control Systems

职业生涯：多智能体决策和控制系统中动态反馈的偏序方法

• 批准号: 1046733
• 负责人: Domitilla Del Vecchio
• 金额: $ 23.68万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1046733&HistoricalAwards=false
• 关键词: CAREER; Partial; Order; Approach; Dynamic

Embedded systems, from automobiles and aircraft to autonomous robots for space exploration, are becoming ubiquitous. A future is envisioned in which large networks of increasingly autonomous embedded systems operate robustly and reliably. Increased levels of automation will require to on-line represent and process huge amounts of data for the design of control schemes that guarantee safety while maintaining performance. A bottleneck in advancement in this direction is complexity. Complexity is established by the natural scale of the system and by the interaction of the physical devices with logic-based control, which create a large number of system behaviors. Current methods in the control synthesis in embedded and hybrid systems usually assume small system size and perfect state measurements. While in some cases such assumptions are satisfied, several realistic applications have large system size and imperfect or partial measurements. To address these problems, this NSF CAREER project is developing a dynamic feedback approach (state estimation plus control) for the monitoring and recovery of multi-agent systems modeled as infinite state transition systems with logic and timed transitions. This approach relies on partial order theory as a key enabler to overcome computational difficulties arising from large system size and from the interaction of continuous evolution and logic. By exploiting partial order structures on the set of states and inputs, this method provides an efficient alternative to enumeration approaches and exhaustive searches, which are common practice in embedded programming. This research is expected to extend our current ability to build provably safe and reliable large-scale multi-agent systems, with potential impact on railway and air traffic control systems, intelligent transportation systems, and large robot teams in adversarial environments

从汽车和飞机到太空探索的自主机器人，嵌入式系统正在变得无处不在。未来的设想是，越来越自主的嵌入式系统的大型网络运行鲁棒性和可靠性。自动化水平的提高将需要在线表示和处理大量数据，以设计在保持性能的同时保证安全的控制方案。在这个方向上前进的瓶颈是复杂性。复杂性是由系统的自然规模和物理设备与基于逻辑的控制的交互建立的，这会产生大量的系统行为。 目前嵌入式和混合系统的控制综合方法通常假设系统规模小，状态测量完美。虽然在某些情况下，这样的假设是满足的，一些现实的应用程序有大的系统规模和不完善或部分的测量。为了解决这些问题，这个NSF CAREER项目正在开发一种动态反馈方法（状态估计加控制），用于监控和恢复多智能体系统，该系统被建模为具有逻辑和定时转换的无限状态转换系统。这种方法依赖于偏序理论作为一个关键的使能器，以克服计算困难所产生的大系统的大小和从连续的进化和逻辑的相互作用。通过利用状态和输入集合上的偏序结构，该方法为枚举方法和穷举搜索提供了一种有效的替代方案，这是嵌入式编程中的常见做法。这项研究有望扩展我们目前构建可证明安全可靠的大规模多智能体系统的能力，并对铁路和空中交通控制系统，智能交通系统以及对抗环境中的大型机器人团队产生潜在影响。