Development of a genetic fuzzy controller for an unmanned aerial vehicle

Abstract

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. © 2011 Springer-Verlag.