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

从汽车和飞机到用于太空探索的自主机器人，嵌入式系统正变得无处不在。对未来的展望是，由越来越自主的嵌入式系统组成的大型网络可以稳健可靠地运行。自动化程度的提高将需要在线表示和处理大量数据，以设计在保证安全的同时保持性能的控制方案。在这个方向上前进的一个瓶颈是复杂性。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相互作用而建立的，这些设备产生了大量的系统行为。在嵌入式和混合系统的控制综合中，目前的方法通常假定系统规模较小且状态测量较好。虽然在某些情况下满足了这样的假设，但一些实际应用具有较大的系统规模和不完美或部分测量。为了解决这些问题，NSF职业项目正在开发一种动态反馈方法(状态估计加控制)来监控和恢复被建模为具有逻辑和定时转换的无限状态转换系统的多智能体系统。这种方法依赖于偏序理论作为一个关键的使能器，以克服由于大系统规模以及持续进化和逻辑的相互作用而引起的计算困难。通过利用状态和输入集合上的偏序结构，该方法提供了一种有效的替代枚举法和穷举搜索的方法，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建立可证明安全可靠的大规模多智能体系统的能力，这将对铁路和空中交通管制系统、智能交通系统以及敌对环境中的大型机器人团队产生潜在影响

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