UAV Controller Design Using Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Khantsis:2005:ausai,

• author = "Sergey Khantsis and Anna Bourmistrova",
• title = "{UAV} Controller Design Using Evolutionary Algorithms",
• booktitle = "Australian Conference on Artificial Intelligence: AI'05",
• year = "2005",
• editor = "Shichao Zhang and Ray Jarvis",
• volume = "3809",
• series = "Lecture Notes in Computer Science",
• pages = "1025--1030",
• address = "Sydney",
• month = dec # " 5-9",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-30462-3",
• DOI = "doi:10.1007/11589990_134",
• size = "6 pages",
• abstract = "Design and optimization of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behaviour. 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.",

}

Genetic Programming entries for Sergey Khantsis Anna Bourmistrova

Citations