Abstract
Manually landing an unmanned aerial vehicle presents unique challenges since unmanned vehicle pilots require extensive training to become proficient in the landing task. Operators must therefore choose between a pilot controlling the vehicle from a ground station, or procurement of an automatic landing system. Although several autoland controllers exist for small or micro unmanned vehicles and for large unmanned vehicles, very few are available for medium sized unmanned aerial vehicles (about the size of a small general aviation aircraft). Additionally, medium sized unmanned aerial vehicles often have limited sensors and instrumentation yet must possess good performance in the presence of modeling uncertainties and external disturbances such as turbulence. This paper describes the synthesis and development of a discrete Quantitative Feedback Theory automatic landing controller for medium size unmanned aerial vehicles. Quantitative Feedback Theory is an attractive control methodology that provides good performance and robustness for a system with structured model uncertainties. It has been successfully applied to many aircraft problems, but not to automatic landing. Controllers for the localizer, glideslope tracker, and automatic flare are developed, as well as details of the inner-loop synthesis. Linear, non real-time six degree-of-freedom Monte Carlo simulation is used to compare the Quantitative Feedback Theory controller to a baseline Proportional-Integral controller in several still air and turbulent conditions. Results presented in the paper show that both controllers show good performance and robustness to model uncertainties in still air, but the Quantitative Feedback Theory controller routinely performs significantly better in all respects in turbulent air. It is therefore concluded to be a promising candidate for an autoland controller.
Published Version
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