Helicopter unmanned aerial vehicles (UAVs) benefit from vertical takeoff and landing, hover, low-speed, and cruising flight capabilities. This versatility has the expense of nonlinear, unstable, and underactuated system dynamics. These challenges and numerous potential applications make the helicopter UAV an interesting testbed for nonlinear control. A platform for such development has been established in the Applied Nonlinear Controls Lab (ANCL). A miniature helicopter was augmented with a manual/autonomous takeover system and the ANCL Avionics. This payload contains a global positioning system (GPS) receiver, inertial sensors, and communications and computing hardware. Allan variance analysis of inertial sensor data enabled the derivation of a GPS-aided inertial navigation system that was implemented on the ANCL Avionics. This extended Kalman filter (EKF)-based algorithm estimates vehicle position, velocity, and attitude necessary for system identification tasks and control system feedback. Performance validation of this algorithm was demonstrated in simulation and in experimental ground and flight tests.
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