Visible to the public Evolutionary strategy approach for improved in-flight control learning in a simulated Insect-Scale Flapping-Wing Micro Air Vehicle

TitleEvolutionary strategy approach for improved in-flight control learning in a simulated Insect-Scale Flapping-Wing Micro Air Vehicle
Publication TypeConference Paper
Year of Publication2014
AuthorsM. Sam, S. K. Boddhu, K. E. Duncan, J. C. Gallagher
Conference Name2014 IEEE International Conference on Evolvable Systems
Date PublishedDec
Keywords1239196, Adaptive Hardware, aerospace components, aerospace control, automated methods, Bioinformatics, correct flight behavior, EA, ES, evolution strategy, Evolutionary algorithm, evolutionary computation, evolutionary strategy, Flapping-Wing Micro-Air Vehicle, flight controller, Force, FW-MAV, genomics, in-flight control learning, learning (artificial intelligence), online evolutionary algorithm, online learning, oscillator learning algorithms, Oscillators, simulated insect-scale flapping wing micro air vehicle, Sociology, Space vehicles, Statistics, Vehicles, wing damage, wing motion patterns, wing-level oscillation patterns

Insect-Scale Flapping-Wing Micro-Air Vehicles (FW-MAVs), can be particularly sensitive to control deficits caused by ongoing wing damage and degradation. Since any such degradation could occur during flight and likely in ways difficult to predict apriori, any automated methods to apply correction would also need to be applied in-flight. Previous work has demonstrated effective recovery of correct flight behavior via online (in service) evolutionary algorithm based learning of new wing-level oscillation patterns. In those works, Evolutionary Algorithms (EAs) were used to continuously adapt wing motion patterns to restore the force generation expected by the flight controller. Due to the requirements for online learning and fast recovery of correct flight behavior, the choice of EA is critical. The work described in this paper replaces previously used oscillator learning algorithms with an Evolution Strategy (ES), an EA variant never previously tested for this application. This paper will demonstrate that this approach is both more effective and faster than previously employed methods. The paper will conclude with a discussion of future applications of the technique within this problem domain.

Citation Key7008742