Controllo di volo in tempo reale: calibrazione del sensore incorporato e acquisizione dati
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Overview
Fonte: Ella M. Atkins, Dipartimento di Ingegneria Aerospaziale, Università del Michigan, Ann Arbor, MI
Panoramica
Il pilota automatico consente di stabilizzare l’aeromobile utilizzando i dati raccolti dai sensori di bordo che misurano l’orientamento, la velocità angolare e la velocità dell’aeromobile. Queste quantità possono essere regolate dal pilota automatico in modo che l’aeromobile segua automaticamente un piano di volo dal lancio (decollo) fino al recupero (atterraggio). Dati di sensori simili vengono raccolti per controllare tutti i tipi di aeromobili, dai grandi aerei da trasporto commerciale ad ala fissa agli elicotteri a rotore multiplo su piccola scala, come il quadricottero con quattro unità di spinta.
Con la posizione inerziale e la velocità catturate da un sensore come il Global Positioning System (GPS), il sistema di controllo del volo in tempo reale con pilota automatico consente a un velivolo multicottero o ad ala fissa di stabilizzare il suo assetto e la velocità dell’aria per seguire una traiettoria prescritta. L’integrazione dei sensori, la calibrazione, l’acquisizione dei dati e il filtraggio del segnale sono prerequisiti per gli esperimenti nel controllo del volo.
Qui descriviamo una suite di sensori che fornisce i dati necessari per il controllo del volo. Vengono descritte le interfacce di segnale e l’acquisizione dei dati su due diverse piattaforme di computer embedded e viene riepilogata la calibrazione del sensore. La media mobile a canale singolo e i filtri mediani vengono applicati a ciascun canale dati per ridurre il rumore del segnale ad alta frequenza ed eliminare i valori anomali.
In questo esperimento, viene dimostrata l’acquisizione dei dati e la calibrazione del sensore per il controllo del volo in tempo reale. Diversi articoli pubblicati hanno descritto i principi della raccolta e del controllo dei dati dei sensori e si sono recentemente concentrati sui sensori per piccoli veicoli aerei senza equipaggio (UAV) [1-3].
Principles
Procedure
Results
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
Figures 9 shows the raw and filtered data of a 30 s lateral acceleration dataset for a quadrotor flying in an indoor environment. The filter values d and n are relatively large to illustrate the impact of the filtering process clearly. As shown, raw data noise is attenuated. However, a notable time delay is present in the filtered data, e.g., in the (attenuated) positive acceleration trend just before t=5 s. For this plot, a small negative bias is noted in the overall acceleration trend; this is likely due to a slight pitch in the IMU mount relative to the quadrotor thruster plane such that a small component of gravitational acceleration is noted in the x-axis acceleration measurement. Such offset is common when aligning sensors only through visual inspection.
Figure 1. Fundamental Forces Acting on Aircraft. Please click here to view a larger version of this figure.
Figure 2. Data Pipeline from Sensors to Flight Control. Please click here to view a larger version of this figure.
Figure 3. Inertial Measurement Unit (IMU) Sensors and Axis Conventions. Please click here to view a larger version of this figure.
Figure 4. Pitot Tube System for Airspeed (V) Measurement. Please click here to view a larger version of this figure.
Figure 5. Five-Hole Probe System for Airspeed (V), Angle of Attack (a), and Sideslip Angle (b) Measurement. Please click here to view a larger version of this figure.
Figure 6. IMU Sensor Calibration with a Single Axis Rate Table. As shown, the z-axis rate gyro voltage can be calibrated directly for each commanded angular velocity, w, and the x-axis accelerometer can be calibrated from centripetal acceleration given angular velocity w and measured radius r from the center of the rate table to the IMU centroid. The IMU can be rotated and remounted to calibrate measurements from the other rate gyro and accelerometer axes. Please click here to view a larger version of this figure.
Figure 7. Quadrotor platform with Beaglebone Blue used for Flight Testing. Please click here to view a larger version of this figure.
Figure 8. Rate Gyro Example Calibration Example. Please click here to view a larger version of this figure.
Figure 9. Example Quadrotor Lateral (x) Accelerometer Data Excerpt for an Indoor Flight using a median filter with d=8 and moving average filter with window n=15. Raw data is indicated by the blue trend, and filtered data is shown in orange. Please click here to view a larger version of this figure.
Figure 10. Example fixed-wing small UAS GPS, Accelerometer, and Rate Gyro Flight Test Data. Raw (unfiltered) data is presented to illustrate the need for signal filtering. Please click here to view a larger version of this figure.
Applications and Summary
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 collection and payload transport. While off-the-shelf sensor packages can be integrated, reliability is critical to understand sensor capabilities and limitations in different environments. For example, heavy precipitation or ice can block pitot tubes, and urban canyon structures can block GPS signals.
Additionally, unusual attitudes can require extension to state estimation computations relying on Euler Angle attitude representations. There is an inherent tradeoff between the resilience gained through the integration of extra sensors and the extra cost and weight required to support redundant sensors. The lowest cost small UAVs will likely continue to employ the baseline suite of sensors for flight control described here. While the most reliable aircraft, such as commercial transport and fighter aircraft, base their state estimates on sensors similar to those described here, they rely on triple redundancy and sensor diversity to assure the aircraft flight controller can rely on an accurate state estimate despite the potential for sensor failures or extremely challenging environmental conditions.
Figure 10 shows sample GPS and (raw) IMU time histories taken from a small fixed-wing UAS flight test. GPS data shows the local loitering pattern manually flown by a pilot through a radio control link. The raw IMU time histories show signal but also exhibit substantial signal noise. This noise results primarily from airframe structural vibrations induced by the propulsion unit (motor) and is typical for fixed-wing small UAS with lightweight wood or composite structures. Note that the data was collected after vibrationally isolating the IMU from the structure with rubber mounts, providing strong motivation for signal filtering. In the time response data, takeoff (launch) occurs just after t=100 s, and landing is seen in the large-magnitude data “spikes” occurring just before t=450 s.
ACKNOWLEDGMENTS
We acknowledge Mr. Prashin Sharma, Mr. Matthew Romano, and Dr. Peter Gaskell of the University of Michigan for their assistance in setting up and conducting experiments.
References
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Transcript
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.