Islands of fitness compact genetic algorithm for rapid in-flight control learning in a Flapping-Wing Micro Air Vehicle: A search space reduction approach

摘要

On-going effective control of insect-scale Flapping-Wing Micro Air Vehicles could be significantly advantaged by active in-flight control adaptation. Previous work demonstrated that in simulated vehicles with wing membrane damage, in-flight recovery of effective vehicle attitude and vehicle position control precision via use of an in-flight adaptive learning oscillator was possible. A significant portion of the most recent approaches to this problem employed an island-of-fitness compact genetic algorithm (ICGA) for oscillator learning. The work presented in this paper provides the details of a domain specific search space reduction approach implemented with existing ICGA and its effect on the in-flight learning time. Further, it will be demonstrated that the proposed search space reduction methodology is effective in producing an error correcting oscillator configuration rapidly, online, while the vehicle is in normal service. The paper will present specific simulation results demonstrating the value of the search space reduction and discussion of future applications of the technique to this problem domain.