Biblio

Previously, we introduced Evolutionary Model Consistency Checking (EMCC) as an adjunct to Evolvable and Adaptive Hardware (EAH) methods. The core idea was to dual-purpose objective function evaluations to simultaneously enable EA search of hardware configurations while simultaneously enabling a model-based inference of the nature of the damage that necessitated the hardware adaptation. We demonstrated the efficacy of this method by modifying a pair of EAH oscillators inside a simulated Flapping-Wing Micro Air Vehicle (FW-MAV). In that work, we were able to show that one could, while online in normal service, evolve wing gait patterns that corrected altitude control errors cause by mechanical wing damage while simultaneously determining, with high precision, what the wing lift force deficits that necessitated the adaptation. In this work, we extend the method to be able to also determine wing drag force deficits. Further, we infer the now extended set of four unknown damage estimates without substantially increasing the number of objective function evaluations required. In this paper we will provide the outlines of a formal derivation of the new inference method plus experimental validation of efficacy. The paper will conclude with commentary on several practical issues, including better containment of estimation error by introducing more in-flight learning trials and why one might argue that these techniques could eventually be used on a true free-flying flapping wing vehicle.

Cyber Physical Systems (CPS) are composed of multiple physical and computing components that are deeply intertwined, operate on differing spatial and temporal scales, and interact with one another in fluid, context dependent, manners. Cyber Physical Systems often include smart components that use local adaptation to improve whole system performance or to provide damage response. Evolvable and Adaptive Hardware (EAH) components, at least conceptually, are often represented as an enabling technology for such smart components. This paper will outline one approach to applying CPS thinking to better address a growing need to address Verification and Validation (V&V) questions related to the use of EAH smart components. It will argue that, perhaps fortuitously, the very adaptations EAH smart components employ for performance improvement may also be employed to maintain V&V capability.