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Real-time Flight Control: Embedded Sensor Calibration and Data Acquisition

Visión general

Source: Ella M. Atkins, Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI

Overview

Autopilot allows aircraft to be stabilized using data collected from onboard sensors that measure the aircraft’s orientation, angular velocity, and airspeed. These quantities can be adjusted by the autopilot so that the aircraft automatically follows a flight plan from launch (takeoff) through recovery (landing). Similar sensor data is collected to control all types of aircraft, from large fixed-wing commercial transport aircraft to small-scale multiple-rotor helicopters, such as the quadcopter with four thruster units.

With inertial position and velocity captured by a sensor such as the Global Positioning System (GPS), the autopilot real-time flight control system enables a multicopter or fixed-wing aircraft to stabilize its attitude and airspeed to follow a prescribed trajectory. Sensor integration, calibration, data acquisition, and signal filtering are prerequisites for experiments in flight control.

Here we describe a sensor suite that provides the necessary data for flight control. Signal interfaces and data acquisition on two different embedded computer platforms are described, and sensor calibration is summarized. Single-channel moving average and median filters are applied to each data channel to reduce high-frequency signal noise and eliminate outliers.

In this experiment, data acquisition and sensor calibration for real-time flight control is demonstrated. Several published papers have described the principles of sensor data collection and control, and they have recently focused on sensors for small unmanned aerial vehicles (UAVs) [1-3].

Procedimiento

This procedure will illustrate IMU and ADS sensor calibration and integration with flight computers and demonstrate the use of integrated INS and ADS data acquisition and processing using in an outdoor flight facility. End-to-end flight control for a quadrotor operating in the University of Michigan’s M-Air netted flight test facility is demonstrated.

1. Sensor Calibration: Inertial Measurement Unit (IMU)

Sensor calibration is most effective whe

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Resultados

Sensor Calibration

An example of a rate gyro calibration plot is shown in Figure 8. In this case, the rate gyro emits a nominal (zero-speed) reading of 2.38 V. Rate gyro voltage data was collected for six different rotational speeds measured in degrees per second, and a linear curve was fit to this data. As shown, the linear fit provides a very good approximation of all collected data points.

Flight Test Results

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Aplicación y resumen

Here we described the sensor systems, data acquisition, and signal filtering process required to enable fixed-wing and rotary-wing aircraft real-time flight control. This data pipeline is an essential element of all manned and unmanned aircraft autopilot systems. Multicopters require autopilots to stabilize, and aircraft of all types critically rely on real-time data acquisition and flight control for all operations as we move toward increasingly autonomous aircraft systems conducting missions involving airborne data col

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Referencias
  1. Langelaan, J.W., Alley, N., and Neidhoefer, J., 2011. Wind field estimation for small unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics, 34(4), pp.1016-1030.
  2. Hallberg, E., Kaminer, I., and Pascoal, A., 1999. Development of a flight test system for unmanned air vehicles. IEEE Control Systems, 19(1), pp.55-65.
  3. Kim, J.H., Sukkarieh, S., and Wishart, S., 2003. July. Real-time Navigation, Guidance, and Control of a UAV using Low-cost Sensors. In Field and Service Robotics, Springer, pp. 299-309.
  4. Gracey, W., 1956. Wind-tunnel investigation of a number of total-pressure tubes at high angles of attack-subsonic, transonic, and supersonic speeds (No. NACA-TN-3641). National Aeronautics and Space Administration (NASA) Langley Research Center, Hampton, VA. (http://www.dtic.mil/get-tr-doc/pdf?AD=ADA377664)
  5. Morrison, G.L., Schobeiri, M.T., and Pappu, K.R., 1998. Five-hole pressure probe analysis technique. Flow Measurement and Instrumentation, 9(3), pp.153-158.
  6. Farrell, J. and Barth, M., 1999. The global positioning system and inertial navigation. New York, NY, USA, McGraw-Hill.
  7. Enge, P., Walter, T., Pullen, S., Kee, C., Chao, Y.C., and Tsai, Y.J., 1996. Wide area augmentation of the global positioning system. Proceedings of the IEEE, 84(8), pp.1063-1088.
  8. Goodrich, M.A., Morse, B.S., Gerhardt, D., Cooper, J.L., Quigley, M., Adams, J.A., and Humphrey, C., 2008. Supporting wilderness search and rescue using a camera‐equipped mini UAV. Journal of Field Robotics, 25(1‐2), pp.89-110.
  9. Rufa, J.R., and Atkins, E.M., 2016. Unmanned aircraft system navigation in the urban environment: A systems analysis. Journal of Aerospace Information Systems, 13(4), pp.143-160.
  10. Paret, D. and Fenger, C., 1997. The I2C bus: from theory to practice. John Wiley & Sons, Inc.
  11. S. Cesnik, C.E., Senatore, P.J., Su, W., Atkins, E.M., and Shearer, C.M., 2012. X-HALE: A very flexible unmanned aerial vehicle for nonlinear aeroelastic tests. AIAA Journal, 50(12), pp.2820-2833.
  12. Vasconcelos, J.F., Elkaim, G., Silvestre, C., Oliveira, P., and Cardeira, B., 2011. Geometric approach to strapdown magnetometer calibration in sensor frame. IEEE Transactions on Aerospace and Electronic Systems, 47(2), pp.1293-1306.
  13. Bovik, A., Huang, T.S., and Munson, D., 1983. A generalization of median filtering using linear combinations of order statistics. IEEE Transactions on Acoustics, Speech, and Signal Processing, 31(6), pp.1342-1350.
  14. Beard, R.W. and McLain, T.W., 2012. Small unmanned aircraft: Theory and practice. Princeton University Press.
  15. Yeo, D., Shrestha, E., Paley, D.A., and Atkins, E.M., 2015. An empirical model of rotorcraft UAV downwash for disturbance localization and avoidance. In AIAA Atmospheric Flight Mechanics Conference, AIAA.
  16. Yeo, D., Sydney, N., and Paley, D.A., 2016. Onboard flow sensing for rotary-wing UAV pitch control in wind. In AIAA Guidance, Navigation, and Control Conference.
  17. Degani, A. and Wiener, E.L., 1993. Cockpit checklists: Concepts, design, and use. Human Factors, 35(2), pp.345-359.
  18. Yeo, D., Henderson, J., and Atkins, E., 2009, August. An aerodynamic data system for small hovering fixed-wing UAS. In AIAA Guidance, Navigation, and Control Conference.
Tags
Real time Flight ControlEmbedded Sensor CalibrationData AcquisitionFixed Wing AircraftAerodynamic LiftAerodynamic DragPropulsion System ThrustWeightStable FlightRoll AxisPitch AxisYaw AxisRotationsGusts Of WindFlight Control SystemMotor And Control Surface CommandsSensorsAltitude MeasurementRoll AnglePitch AngleYaw AngleAir SpeedData FilteringOutliersProcessed Data QualityAircraft State EstimationFlight ControlMulticoptersInertial Measurement Unit IMUAccelerometersRate GyroscopesMagnetic Field Sensors

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Concepts

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Calibration of IMU

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Real-time Flight Experiment

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