MG
M.W. Goedhart
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Intelligent Flapping Wing Control
Reinforcement Learning for the DelFly
Master thesis
(2017)
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Menno Goedhart, Erik-jan van Kampen, Sophie Armanini, Coen de Visser, Alexei Sharpans'kykh, Qiping Chu
Flight control of the DelFly is challenging, because of its 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. Furthermore, 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.
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Flight control of the DelFly is challenging, because of its 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. Furthermore, 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.
Bachelor thesis
(2015)
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M.S.T. van den Aarssen, L.M.A. Declerck, M. Dirkzwager, R.J. Everaert, M.W. Goedhart, A.A.E. Naorniakowski, J.M.F. van Neerven, E.A. Schouten, K. Schreiber, K.F. Yip, M.D. Pavel, J. Carvajal Godínez, T. Visser