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

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

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