Efficient and resilient micro air vehicle flapping wing gait evolution for hover and trajectory control

•A bioinspired, search-efficient, tunable optimization scheme is adapted for control.•Micro Air Vehicle (MAV) flapping wing gaits are evolved on-line, model-independently.•Scheme properties are benchmarked in a case study of evolution for MAV hover control.•Scheme speed and responsiveness compare favorably to related evolutionary methods.•A second study attains MAV trajectory control in unsteady flow with little computing.

This paper deploys a recently proposed, biologically inspired, on-line, search-based optimization technique called Selective Evolutionary Generation Systems (SEGS) for control purposes; here, to evolve Micro Air Vehicle (MAV) flapping wing gaits in changing flight conditions to maintain hovering flight and track trajectories in unsteady airflow. The SEGS technique has several advantages, including: (1) search-efficiency, by optimally trading off prior search space information for search effort savings as quickly as possible in dynamic environments; (2) model-independence, as in biology, avoiding biases induced by built-in models rendered incorrect by environment changes; and (3) resilience, through sufficiency for stochastic behavior that is itself sufficient for responsiveness to search-objective variations caused by environment fluctuations. This work presents the first approach that can simultaneously evolve optimal MAV flapping wing gaits efficiently and resiliently, adapt on-line, and, via model-independence, allow feedback from either experimental sensors or alternate external models (affording control versatility for hover or forward flight, unsteady or quasi-steady aerodynamics, and any dynamics or wing kinematics). Performance benchmarks are also provided. Because the (1+1)-Evolution Strategy (ES) and the Canonical Genetic Algorithm with Fitness Proportional Selection (CGAFPS) are two SEGS special extreme cases, an additional comparison showcases SEGS possession of both (1+1)-ES computational speed and CGAFPS resilience.