1. Status: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018 (accepted).
2. Author : Jonggu Lee, Seungwan Ryu, Taewan Kim, Wonchul Kim and H. Jin Kim
3. Abstract: Flapping-wing micro air vehicles (FWMAVs) become promising research platforms due to their advantages such as various maneuverability, and concealment. However, periodic and unsteady airflows generated by flapping-wing motion make their dynamics time-varying and highly nonlinear. Therefore, simple model-free controllers are widely used, but they are impractical to track diverse flight trajectories and require many trials and errors for gain-tunings. In this paper, we suggest a model-based control strategy for FWMAV using learning architecture. For this task, we construct a ground station for logging flight data and control inputs, and train dynamics with a neural network. Then, we apply model predictive control (MPC) to the trained model. We validate our method by hardware experiments.
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