Development of a Genetic Fuzzy Controller for an Unmanned Aerial Vehicle
Autonomous Unmanned Aerial Vehicles (UAVs) have been increasingly employed by researchers, commercial organizations, and the military to perform a variety of missions. This paper discusses the design of an autopilot for an autonomous UAV using a messy genetic algorithm for evolving fuzzy rules and fuzzy membership functions. The messy genetic algorithm scheme has been adopted because it satisfies the need for flexibility in terms of the consequents applied within the conditional statement framework used in the fuzzy rules. The fuzzy rules are stored in a Learning Fuzzy Classifier System (LFCS) which executes the fuzzy inference process and assigns credit to the population during flight simulation. This framework is useful in evolving a sophisticated set of rules for the controller of a UAV, which deals with uncertainty in both its internal state and external environment.
KeywordsUnmanned Aerial Vehicle Genetic Algorithm Fuzzy Control
Unable to display preview. Download preview PDF.
- 5.Hoffmann, F., Pfister, G.: Genetic Evolutionary Learning of a Fuzzy control Rule Base for an Autonomous Vehicle. In: Proceedings of the Fifth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based System (IPMU 1996), pp. 659–664 (1996)Google Scholar
- 6.Tan, K., Lee, T., Lee, L.: A Messy Genetic Algorithm for the Vehicle Routing Problem with Time Window Constraints. In: Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001), pp. 679–686 (2001)Google Scholar
- 7.Shu, L., Schaffer, J.: HCS: “Adding hierarchies to classifier systems”. In: Proceedings of the Fourth International Conference on Genetic Algorithms, Los Altos, CA, pp. 339–345 (1991)Google Scholar
- 8.Van Veldhuizen, D.A.: Genetic Multiobjective Optimization with Messy Genetic Algorithms. In: Proceedings of the Fifteenth ACM Symposium on Applied Computing (SAC 2000), pp. 470-476 (2000)Google Scholar
- 9.Riley, J., Ciesielski, V.: Evolving Fuzzy Rules for Reactive Agents in Dynamic Environments. In: Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002), Singapore, pp. 124–130 (November 2002)Google Scholar
- 10.Liska, J., Melsheimer, S.S.: Complete design of fuzzy logic systems using genetic algorithms. In: Proceedings of Third IEEE International Conference on Fuzzy Systems, pp. 1377–1382. IEEE, Piscataway (1994)Google Scholar
- 12.Beard, R.W., McLain, T.W.: An Introduction to Autonomous Miniature Air Vehicles. Brigham Young University (2007)Google Scholar
- 13.Christiansen, R.S.: Design of an Autopilot for Small Unmanned Aerial Vehicles. Master Thesis in Electrical and Computer Engineering at Brigham Young University (August 2004)Google Scholar
- 14.Sorton, E., Hammaker, S.: Simulated Flight Testing of an Autonomous Unmanned Aerial Vehicle Using Flightgear. Institute for Scientific Research, Fairmont, West Virginia (September 2005)Google Scholar
- 15.Stevens, B.L., Lewis, F.L.: Aircraft Control and Simulation, 2nd edn. John Wiley & Sons, Inc., Chichester (2003)Google Scholar