A Grey-Modeling Research on a Small-Scale Autonomous Helicopter

This paper describes a grey-modeling method for small-scale unmanned helicopter that combines the advantages of first-principles modeling and system identification. First, a structure of the attitude model at hovering is developed by strict mechanism derivation and the unknown structural parameters are got. Then, a flying experiment was implemented to get the input and output data. Next, a method combining subspace model identification method and prediction error method is proposed to identify the attitude model and the key parameters of the model are obtained. Finally, comparisons between predicted responses and flight experiments at hovering show excellent agreement. The results of simulation suggest that the grey- modeling method is applicable for modeling a small-scale helicopter. Small-scale autonomous helicopters have the advantages of size, agility and maneuverability, which require a high bandwidth control system for full authority autonomous performance. However, a problem of importance for the flight control of autonomous helicopter is its inherent qualities: the dynamics of the helicopter are essentially unstable, there are nonlinear variations in dynamics with air speed, moreover, the helicopter has six degrees of freedom in its motion which is multiple-input multiple-output (MIMO) system and its flight modes are cross-coupled. Most multivariable control methods are model-based, and the dynamic model for a small-scale helicopter which is simple enough to be practical for controller is not readily available (1, 2).