Drag Force Fault Extension to Evolutionary Model Consistency Checking for a Flapping-wing Micro Air Vehicle
Published In
Evolutionary Computation (CEC), 2016 IEEE Congress on
Document Type
Citation
Publication Date
11-21-2016
Abstract
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.
Locate the Document
DOI
10.1109/CEC.2016.7744292
Persistent Identifier
http://archives.pdx.edu/ds/psu/20108
Citation Details
J. C. Gallagher, M. Sam, S. Boddhu, E. T. Matson and G. Greenwood, "Drag force fault extension to evolutionary model consistency checking for a flapping-wing micro air vehicle," 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, pp. 3961-3968.