UAV controller design using evolutionary algorithms

Authors:
Sergey Khantsis;Anna Bourmistrova
Affiliations:
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC, Australia;School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, Melbourne, VIC, Australia
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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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.