Small size helicopters pose a particular challenge when compared to their full size counterparts. Characteristic lower damping results in greater instability, while small payloads logistically limit sensor types, and more importantly sensor quality. These issues are further exacerbated when using a size 60 (payload ~ 5kg) compared to the R50 (payload ~20kg) used by many research groups. Traditional control usually necessitates the development of a plant model, or tedious gain tuning; the required accuracy of these models increases for non-linear approaches such as linearisation feedback. Neuro/fuzzy systems, on the other hand, can use learning strategies to avoid the need for a system model. Proving stability, however, can be difficult. Both traditional and non-traditional control methods will be applied to this platform. In so doing, a direct comparison between both classes of control strategies can be made. This paper details the progress to date, toward developing a small autonomous helicopter. We describe system architecture, avionics, visual state estimation, custom IMU design. aircraft modelling, as well as various linear and neuro/fuzzy control algorithms. Experimental results are presented for state estimation using fused stereo vision and IMU data. heading control, and attitude control. FAM attitude and velocity controllers have been shown to be effective in simulation.
Sensing and control for a small-size helicopter
Sensorik und Regelung für einen kleinen Hubschrauber
2002
11 Seiten, 8 Bilder, 24 Quellen
Conference paper
English
Sensing and Control for a Small-Size Helicopter
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