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 when performed with support from high-quality test equipment. For the 3-axis IMU, calibrate the rate gyro and accelerometer for each axis separately using a precision rate table (Figure 6). The rate table precisely rotates at a user-defined angular velocity. The user issues a series of rate commands, during which the IMU collects the data needed for sensor calibration. The single-axis calibration experiment described below is therefore repeated three times, once for each IMU sensor axis (x, y, z).
(9)2. Quadrotor Flight Experiments
For our final series of experiments, we mount the IMU and pitot system on a quadrotor (shown in Figure 7) and fly in the University of Michigan’s M-Air netted flight facility. The vehicle is stabilized through a port of the Ardupilot open source autopilot package to the Beaglebone Blue (no microprocessor used) and configured before flight through the Mission Planner ground station software. A radio-control transmitter/receiver interface enables the pilot to provide “outer loop” commands for quadrotor altitude, side-to-side motion, and heading to Ardupilot’s “inner loop” flight control law regulating quadrotor roll angle, pitch angle, yaw angle (heading), and altitude. [14]
Because a quadrotor does not require airspeed feedback to stabilize, Ardupilot only relies on IMU data plus a pressure sensor for altitude, which is calibrated during program initialization relative to the takeoff altitude pressure, to stabilize flight given pilot inputs. A fully autonomous extension of Ardupilot requires inertial position data from GPS or other sensing system (e.g., high-speed motion capture). Because our experiments were performed with quadrotors in constrained environments, the pitot air data system is not necessary. However, pitot systems are essential for fixed-wing aircraft and multicopters attempting precise flight paths following uncertain windy environments. [15, 16] The flight test procedure is divided into three phases: pre-flight, flight test, and post-flight. This subdivision is similar to the procedures followed by pilots of manned aircraft through the use of well-established cockpit checklists. [17]
Pre-flight
Flight Test
Post-flight
A fixed wing aircraft achieves steady flight by balancing four forces: aerodynamic lift, aerodynamic drag, propulsion system thrust and weight. To achieve stable flight, it must also balance moments about all three axis, the roll, pitch and yaw axis. All rotations are defined as angles about these axis with changes in the roll axis causing side-to-side motion, changes to the pitch axis causing forward and backward tilting motion and changes in the yaw axis causing heading changes.
In order to stabilize the aircraft to any sudden changes like gusts of wind, a flight control system issues motor and control surface commands that must be updated in real-time. Thus, the control system uses various sensors to maintain an accurate measurement of current altitude, meaning the roll, pitch and yaw angles, as well as the air speed. Once data is acquired from the sensors, the signals are filtered to reduce the impact of noise and outliers on processed data quality. The data is then aggregated into a full estimate of aircraft state and used for flight control.
Both fixed wing aircraft and multicopters rely on this control system to monitor and control aircraft altitude. Both also utilizes sensor sweep known as an inertial measurement unit or IMU.
An IMU typically consists of three sensor types: accelerometers to measure linear acceleration, rate gyroscopes to measure angular velocity and magnetic field sensors to measure the direction and strength of the local magnetic field. An IMU is often coupled with a GPS system and mounted near the aircraft center of gravity with the sensor axis aligned with the axis of the aircraft body.
In this lab, we will demonstrate the calibration of a simple IMU using a precision rate table. We'll then mount the calibrated IMU to a multicopter and perform a flight test to view real time and filter data.
In the first part of the experiment, we will calibrate the IMU which contains a rate gyro and accelerometer for each axis using a precision rate table. The rate table precisely rotates at a user defined velocity following a series of rate commands. This enables us to determine the relationship between the voltage readout and velocity.
To begin, mount the IMU on the rate table with screws and orient it such as that the sensor axis being calibrated in this case the X-axis, is directly radially inward or outward. Measure the distance from the table center to the IMU center and use this measurement as the reference radius for circular motion. The IMU is mounted on a data acquisition board. Connect the components directly.
Now, set up the software to collect the IMU rate and acceleration data. Conduct a series of experiments with different positive and negative constant rate table rotation rates with zero used as the baseline measurement. While the rate table is motionless, record the rate gyro and accelerometer by S values. Then, initiate the test and collect the data.
Once all the angular velocities have been tested for that orientation, detach the IMU and reposition it such that the accelerometer is oriented upward. Reattach it, then initiate the test to collect -1 G data. After that, flip the IMU so that the accelerometer is oriented downward and collect +1 G data.
When you have completed the calibration of the x-axis, reposition the IMU so that the z-axis sensor is orientally radially outward and repeat all tests, remembering to position the IMU upwards and downwards to calibrate the accelerometer. Perform the same procedure for the y-axis sensor.
In the next part of the experiment, we will mount the IMU on the quadrotor and fly it inside of a netted flight facility. A radial control transmitter receiver interface enables the pilot to provide commands for altitude, heading, roll angle, pitch angle and yaw angle.
Before starting, charge all batteries and test the components prior to installation on the quadrotor. Then prepare the flight making sure that at least three people, the pilot in command, the visual observer and the ground station operator are all briefed on the flight plans. Bring the quadrotor into the netted flight facility and set it on a flat landing board.
The flight test begins with take off from the origin climbing to a 1.5 m altitude. Then, we'll execute a two meter square flight pattern with a 0.5 m/s reference velocity. The quadrotor pauses prior to each change of position. Then we'll execute segments of higher speed traversals at 0.5, 1, and 1.5 m/s to demonstrate how velocity impacts overshoot.
To begin the flight test, start the data acquisition on the ground station. After confirming that the flight area is clear, arm the motors. Now, initiate the flight test sequence with the pilot calling out each step before performing them beginning with takeoff. Be sure to announce all flight mode changes, known waypoint targets, or maneuvers.
After the flight plan has been executed, alert the rest of the flight team of the final descent and landing of the quadcopter. Then, disarm the motors on the quadcopter. Save and download all flight data and log the flight in the flight logbook. Finally, recover all equipment and clear the area for the next user.
Now let's interpret the results. Starting with the calibration data for the IMU, first we show a plot of rotational speed of the rate table versus the gyro voltage. Note that the rate table provides direct control of angular velocity for the gyro calibration. A linear fit to the data enables the calculation of speed from gyro voltage. In this case, the rate gyro emits a nominal zero speed reading of 2.38 volts.
Finally, let's look at the flight data. Here we show a 30 second lateral acceleration data set for the quadrotor using our calibrated IMU. This plot shows raw and filtered acceleration measurements from the IMU versus time. The data was filtered in order to remove noise from the measurement. You can see that raw noise data is attenuated. However a time delay is present in the filtered data.
In summary, we learned how aircraft control systems use various sensors to measure current altitude and airspeed during flight. We then calibrated a rate gyro and accelerometer and mounted them on a quadrotor before performing flight experiments.
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