Conventional calibration of an inertial measurement unit (IMU) through open-loop data collection includes typical flight simulator systems that provide processed stimuli to emulate real-life flight conditions. Other solutions involve testing inertial measurement devices on a multi-axis rate table using a processor internal to the inertial measurement devices, and transferring the signals directly to the processors for determining and storing the calibration coefficients of the inertial measurement devices internally so that they are self-calibrating. Unfortunately, conventional solutions typically involve evaluating control algorithms in a computer simulation before experimentation in the aircraft occurs. This can result in unstable flight during the first few cycles that could lead to failure of the aircraft.

An apparatus for training an autopilot to fly a simulated aeronautical vehicle was developed that comprises a three-degrees-of-freedom gimbaled platform with a pitch axis, roll axis, and yaw axis; an autopilot component operatively connected to the platform in which the autopilot component comprises an inertial measurement unit; and motors that rotate the platform along the pitch, roll, and yaw axes.

Shaft encoders operatively connect to the motors and calculate an angular position of the platform. Motor drivers are operatively connected to the motors. A microcontroller is connected to the motor drivers, and a quadrature encoder is connected to the shaft encoders and the microcontroller. Sensors generate and sense environmental conditions affecting the platform and the autopilot. A flight simulator module is connected to the microcontroller, and a computer executes the flight simulator module, causing actuation of the platform. The sensors generate environmental conditions and cause the autopilot component to react to those conditions.

The apparatus could include a planetary gearbox connected to the motor that controls movement of the platform along the yaw axis, as well as a temperature- and pressure-regulating enclosure that encases the platform. It could also comprise a means for forcing air into and drawing air out of the enclosure. The environmental conditions could include global positioning system bearings, wind speed, barometric pressure, temperature, humidity, and electromagnetically generated headings.

For more information, contact Dan Swanson at This email address is being protected from spambots. You need JavaScript enabled to view it. ; 406-994-7736.