Modeling, identification, and trajectory planning for a model-scale helicopter

There has been a great deal of interest in the dynamics and control of unmanned helicopter robots since the last decade, as such unmanned aerial vehicles (UAV) are being re-discovered. They are an excellent cost-effective and safe way to replace human operators/pilots in military, civilian, and commercial areas when there exist significant threats to human lives, or when the environment is not suitable for large human-carrying vehicles.; A mathematical model for a model-scale unmanned helicopter robot, with emphasis on the dynamics of the flybar is first presented. The model is based on a rigid-body description of the helicopter, with four actuation inputs representing the four stick positions available to the remote-control pilot. The interaction between the flybar and the main rotor blade is explained in detail; it is shown how the flapping of the flybar increases the stability of the helicopter robot as well as assists in its actuation. Working from first principles and basic aerodynamics, the equations of motion for the helicopter and flybar are derived.; The second part of this dissertation presents system identification experiments for the model helicopter. The results verify the mathematical model structure described above. They are used to identify model parameters such as inertias and aerodynamic constants by directly taking the nonlinear model structure into account.; Autonomous vehicles with nonlinear dynamics such as the model helicopter need to have a planned reference trajectory and a feedback controller to accomplish the task of traveling from a launch point to a goal point. A general methodology for finding feasible and approximately optimal trajectory without violating the state and input bounds is presented. Examples of single and multiple waypoint trajectory generation cases based on a simplified nonlinear longitudinal helicopter model with minimum time criteria are included. It is also shown why the planning method requires a closed-loop controller, and our trajectory generation method is superior over traditional point-stabilization control method. This dissertation concludes with a brief discussion of future work.