Methodologies for Engineering with Plug-and-Learn Components- Synthesis and Analysis Across Abstraction Layers

Cyber-Physical Systems (CPS) that contain self-modifying smart components can improve and self-repair, but sometimes at the cost of impeding model-based Verification and Validation (V&V). In this work, we focus on maintaining short and long range V&V capability in a system containing self-adaptive smart components. In this work, we focus on smart component based in-flight control adaptation of damaged Flapping-Wing Micro Air Vehicles (FW-MAV). Each of our three partner institutions is making a related, but distinct, attack on the problem of encapsulating adaptation into "plug-and-learn" modules and using them to adapt flight control in a way that enables, rather than destroys, V&V capability. Each project partner institution is, additionally, focusing on a different level of abstraction in the system's control abstraction hierarchy. Intellectual Merits: We provide fundamental research on extracting actionable information by exploiting relationships across abstraction levels of an engineered artifact. To the best of our knowledge, this is an unusual, and perhaps unique, undertaking. Broader Impacts: We are developing new methods of construction and control of flapping wing robots. It is further likely that the methods we develop will be useful in maintaining actionable knowledge in other cyber physical systems.