Autoland controllers are prevalent for both large and small/micro unmanned aerial vehicles, but very few are available for medium sized unmanned aerial vehicles. These vehicles tend to have limited sensors and instrumentation, yet must possess good performance in the presence of modeling uncertainties, and exogenous inputs such as turbulence. Quantitative Feedback Theory is an attractive control methodology for this application, since it provides good performance and robustness for systems with structured model uncertainties. It has been successfully applied to many aircraft problems, but not to automatic landing, and only inner-loop synthesis has been presented in the literature. This paper describes the synthesis and development of an automatic landing controller for medium size unmanned aerial vehicles, using discrete Quantitative Feedback Theory. Controllers for the localizer, glideslope tracker, and automatic flare are developed, with a focus on the outer-loops 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 landing scenarios. The Quantitative Feedback Theory controller provides performance similar to the Proportional-Integral controller in still and in turbulent air. Both controllers show similar robustness to turbulence, but the Quantitative Feedback Theory controller provides significantly better robustness to model uncertainties in turbulent air as well as to sensor characteristics in turbulence. Based on the results of the paper, the QFT controller is a promising candidate for an autoland controller.

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