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