Machine Learning for Flapping Wing Flight Control

Abstract

Flight control of Flapping Wing Micro Air Vehicles is challenging, because of their complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. A novel Classification Algorithm for Machine Learning control (CAML) is presented, which uses model identification and a neural network classifier to select from several predefined gain sets. The algorithms show comparable performance when considering variability only, but the Policy Gradient algorithm is more robust to noise, disturbances, nonlinearities and flapping motion. CAML seems to be promising for problems where no single gain set is available to stabilize the entire set of variable systems.