Abstract
Design and optimization of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behavior. In this study, EAs are used to design a controller for recovery (landing) of a small fixed-wing UAV (Unmanned Aerial Vehicle) on a frigate ship deck. This paper presents an approach in which the whole structure of the control laws is evolved. The control laws are encoded in a way common for Genetic Programming. However, parameters are optimized independently using effective Evaluation Strategies, while structural changes occur at a slower rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is demonstrated and a set of reliable algorithm parameters is identified.
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Khantsis, S., Bourmistrova, A. (2005). UAV Controller Design Using Evolutionary Algorithms. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_134
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DOI: https://doi.org/10.1007/11589990_134
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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