A Comprehensive Sensor Suite for MiniAerial Vehicle Flight Tests

Prateek Jolly∗, Vaibhav V Unhelkar†, and Hemendra Arya‡∗ Research Assistant, email: jollyboy1@iitb.ac.in† Research Assistant, email: v.unhelkar@iitb.ac.in‡ Associate Professor, email: arya@aero.iitb.ac.in

Department of Aerospace Engineering, Indian Institute of Technology Bombay, Mumbai - 400076

Abstract—Flight tests, which are primarily usedfor model creation and validation, are an importantpart of the aircraft design and development. Forlarger aircraft, system identification methods based onflight tests have been studied in detail and are wellestablished. However, flight tests of mini unmannedaerial vehicles (MAV) pose new challenges, chieflybecause the small, cheap and lightweight sensorsused in MAV instrumentation are not as accurateas those used on larger aircraft. In this paper, wepresent a comprehensive sensor suite for mini aerialvehicles with specific focus on construction, calibrationand implemention of the sensors. The sensor suite iscapable of measuring the important flight variables- thrust, angle of attack, side slip, airspeed, attitude,inertial rates, altitude, position, velocity, accelerationand actuator deflections - and can be used for flighttests. The paper concludes with a flight data based sys-tem identification methodology, along with simulatedresults, to arrive at the aerodynamic models of miniaerial vehicles.

I. INTRODUCTION

Mini unmanned aerial vehicles are remotely pi-loted; sub meter span aircraft that are being widelyused for aerial reconnaissance and sensing. Theirsmall size gives them a distinct advantage over theirlarger counterparts, in that they are easier to trans-port, deploy, and do not require special landing andtakeoff facilities. Their low noise electric motors,small size and low velocities also ensure that theyare not easy to detect; but, at the same time, theyhave short endurance and can carry limited payload.Hence, there is a need to optimize the design of aMAV to achieve the desired performance. To extractthe optimal performance from these aerial vehicles,their accurate modelling is necessary.

Much technical literature is available to helparrive at the dynamic model of aircraft based onempirical (thumb-rule based) and/or analytical re-lations. Further, computational techniques, such asthose of Computational Fluid Dynamics, are alsoused while modelling an aircraft. However, both theanalytical and computational techniques suffer fromlimitations while modelling mini aerial vehicles; thehighly unconventional designs of MAVs often pre-vent use of empirical results, and the computationaltechniques require domain expertise and may betime consuming. To this end, system identificationbased on flight tests can be used to model theaircraft, and predict various forces and moments thataffect the aircraft’s performance.

For larger aircrafts, methods based on flight testshave been studied in detailed and are well estab-lished ([1], [2], [3]). On similar lines, flight data canbe used to model the smaller class of aerial vehicles.However, their short design cycles, reduced cost andnoisy on-board sensors, pose new challenges for us-ing flight test based methods for MAVs [4]. Variousresearch studies have been reported in the literaturefor system identification of UAVs and MAVs, withfocus on different aspects of the problem ([4], [5],[6], [7]). For instance, wind tunnel tests have beencarried out, albeit without any flight test validation,to determine the lift, drag and moments acting ona MAV [6]. Specifically, Ref. [7] outlines a sensorsuite that measures airspeed, altitude, attitude andposition. The autopilot based on this sensor suiteuses manually tuned PID controller to navigate theaircraft, since additional wind tunnel based testsare required for parameter estimation in absence of

α, β, and thrust measurements. Hence, there is aneed of a comprehensive sensor suite tailored forMAVs, which can be used to gather important flightvariables, namely, thrust, angle of attack, side slip,airspeed, attitude, inertial rates, altitude, position,velocity, acceleration and actuator deflections. Fur-thermore, accuracy and applicability of flight testbased system identification methods for MAVs alsoneed to be investigated.

In this paper, we describe the design, implemen-tation and calibration of a novel thrust measurementsystem, and α and β measurement vanes for MAVs.Details of additional commercially available sensors- Inertial Measurement Unit, GPS receiver, altitudesensor, airspeed sensor - required to measure otherrequired flight variables have also been provided.The paper concludes with a flight data based systemidentification methodology, along with simulatedresults, to arrive at the aerodynamic models of miniaerial vehicles.

II. SENSOR SUITE FOR MAVS

The parameters affecting the aircraft can be clas-sified into the following three groups,

1) Inertia related: These include variables suchas the mass, moment of inertias, and position of cen-ter of gravity of the aircraft. These terms contributeto the forces and moments caused due to the Earth’sgravity.

2) Propulsion: These include variables such asthe angular velocity of the propeller, and the asso-ciated thrust.

3) Aerodynamic: These include the lift and dragassociated with the aircraft, along with the aerody-namic moments produced by the aircraft structureas well as its control surfaces.

The inertia parameters can be determined a priori,however, determination of thrust and aerodynamicforces requires in flight measurements. To com-pletely characterize these forces and to determinea model of the aircraft one needs to measure thrust,angle of attack, side slip, airspeed, attitude, inertialrates, altitude, position, velocity, acceleration andactuator deflections. This can be achieved withthe comprehensive sensor suite, which is brieflydescribed in Table I. The following sections describethe design, calibration and implementation of theindividual sensors.

TABLE ICALIBRATION CURVE OF THE FLEX FORCE SENSOR

A. Thrust

Conventionally, in flight thrust has been estimatedusing a motor-propeller model, which is obtained viawind tunnel tests [5]. The problem with such a setupis that it usually ignores the effects of fuselage andwing blockage, which tend to reduce the appliedthrust. To overcome this limitation, a novel thrustmeasurement device (Fig. 1) has been developed,which uses a highly sensitive piezometric flexi-forceresistor [8] as its sensor element.

Fig. 1. Thrust Measurement Sensor

Fig. 2. Thrust Measurement Sensor

The flex force element behaves as a pressuresensor whose resistance varies with compressiveloading. The force produced by the propeller, whichis tensile in nature, is transferred to the fuselagethrough the motor mount. A mechanism to convertthis tensile force into a compressive force is devel-oped (Fig. 2). The sensor is sandwiched betweentwo plates. A foam backing is used to distribute thepressure evenly. The force produced by the motor istransferred to the sensor via the connecting rod andthe backplate. The change in force per unit area issensed and is reflected as a change in the resistancebetween the leads of the sensor.

The plates are constructed of 4 mm aluminiumsheets to keep the weight to a minimum. The insideof the bearings are smeared with low density greasefor smooth operation. The sensor is pre-compressedby 125 grams, to measure the drag produced bythe freewheeling propeller can be measured. Thecalibration curve for the sensor is shown in Fig. 3.

Fig. 3. Calibration Curve of the Flex Force Sensor

Two low pass filters are connected in series to theoutput of the sensor to dampen the high frequencynoise generated by mechanical vibrations of themotor. Fig. 4 shows the effect of the filter on themeasured raw data.

Fig. 4. Signal Improvement using Low Pass Filters

B. Aerodynamic Angles, α and β

The orientation of the aircraft with respect tothe free stream velocity is of high importance, asit directly affects the resultant aerodynamic forceexpereinced by the aircraft. Two wind vanes are usedto measure the aircraft’s angle of attack (α) and sideslip (β), respectively (Fig. 5).

Alpha and Beta Vanes are small wind vanes thatare mounted on the wingtips. They point into thewind and thus the angle between the wing chordand the wind vane measures alpha and beta. Theadvantage of using wind vanes is that they are veryeasy to calibrate and do not have any limitation ontheir operating range. Their downside is that they areheavier, more obtrusive and the inertia of the vanesand the fiction inherent in the measuring instrumentimplies a higher dead zone than the multi-holeprobes [9].

Fig. 5. Alpha and Beta Vanes

The vanes manufactured for use on the miniUAV are constructed of 1mm strips of carbon fiberas the boom, and 2mm balsa as the vane. Thislight construction ensures a low moment of inertia.Further, two β vane mounts have been used, oneither side of the wing, to maintain symmetry. Low

friction ball bearings are used to reduce the deadzone. A UNIPOT PS22C [10] high sensitivity lowfriction, 5 kilo-ohm potentiometer is used as thesensor element. The potentiometer, being of 22mmdiameter is mounted in an airfoil shaped mount toreduce drag and support the 5mm diameter carbonfiber cantilevered rod. Fig. 6 shows the calibrationcurves for the α vane.

Fig. 6. Alpha Vane Calibration

C. AttitudeThe attitude of the aircraft is defined as the orien-

tation of the aircraft with respect to the horizon. Theattitude is measured using an inertial measurementunit developed by IdeaForge [11], Fig. 7. The IMUuses MEMS based accelerometers to measure linearaccelerations, and gyroscopes to measure angularaccelerations. The output of the IMU is a processedand filtered signal, containing attitude, angular rates,and specific accelerations of the aircraft.

Fig. 7. GPS Receiver and IdeaForge IMU

D. Position, Heading and Ground SpeedA GPS receiver (Fig. 7) is used to obtain estimates

of velocity, heading and position of the aircraftat a 5Hz update rate. The accuracy of velocityand heading measurements is 0.1 m/s and 0.5 deg,respectively.

E. Air SpeedAirspeed is measured using a MPXV7002DP[12]

differential pressure sensor (Fig. 8). One nozzle ofthe sensor is connected to a thin steel pitot tube,through a flexible rubber tube. The pitot tube ispositioned so as to be unaffected by both the airflowover the wing and the prop wash. The sensor has arange of 4 kPa, and an analog output varying from0.5 to 4.5 volts when operated at its maximum ratedvoltage of 5V.

Fig. 8. Airspeed Sensor

The sensor is calibrated using a U-tube Manome-ter and the Eq. 1. The sensor senses the changein pressure, which is directly proportional to thevoltage variation (δVt), with k as the proportionalityconstant. Fig. 9 shows the calibration curves for theairspeed sensor.

(hρg)water = (1/2ρV 2)air (1a)

δVt = kV 2air (1b)

Fig. 9. Airspeed Sensor Calibration

F. AltitudeThe altitude is measure using a MPX5100AP[13]

absolute pressure sensor. The change in voltageacross the pressure sensor is amplified using a differ-ence operational amplifier to increase the readabilityof the sensor. The sensor is calibrated by using a U-tube manometer, and converting readings in head ofwater to head of air. Fig. 10 shows the calibrationcurve of the altitude sensor.

Fig. 10. Altitude Sensor Calibration Curve

G. Actuator Deflections

The control surfaces are actuated by micro ser-vos, which operate using a pulse width modulated(PWM) signal at 5V. The servo motors are con-nected through mechanical linkages to the controlsurfaces, so as to convert their rotary motion intolinear motion, and back to rotary motion. Thedeflections of the control surfaces are thus directfunctions of the PWM value. These PWM signalsare recorded and suitably processed to measure thecontrol surface deflections.

H. Data Storage

Fig. 11. Autopilot and Data Aquisition Board

In order to measure, record and process the mea-surements from various sensors described above, anon-board data acquisition system is required on. Thedata acquisition system is integrated with the MAV’sautopilot system onto a single custom board (henceforth referred to as the autopilot board, Fig. 11).The board uses a MAC 7112 microcontroller[14]as its processing unit and a 2 GB micro-SD datacard as the memory storage device for the dataacquisition system. The airspeed and altitude sensors

are directly mounted on the board. The microcon-troller has 16 multiplexed analog to digital converterpins which are connected to each of our measuringinstruments. The DAQ records data at a frequencyof 50Hz. The boards weighs 15 grams along withthe airspeed and altitude sensors mounted on it.The data recorded using this comprehensive sensorsuite, system identification of the aircraft can beperformed.

III. SYSTEM IDENTIFICATION

Having described a complete sensor suite forflight tests of mini aerial vehicles, we explain anddemonstrate through simulation an offline, time-domain based technique for system identificationof aircrafts using the measured and recorded flightdata. Knowledge of accurate mathematical modelsof an aircraft can help drastically improve its design,capability and operating efficiency. For instance, agood model can help the aircraft designer to predict,via simulation, the achievable performance prior tofinalizing the design or mission.

The system identification presented in this sectionare limited to fixed-wing aircrafts, and assume no orminimal wind disturbances during the flight tests.Further, we have reduced the problem of systemidentification to that of parameter estimation ofaerodynamic models, and present the analysis onlyfor longitudinal motion of the aircraft. Detaileddescription of system identification methods foraircrafts can be found in [1], [2]. Reference [4]provides an overview of additional challenges facedin system identification of small aerial vehicles.

A. Methodology

System identification can be carried out either intime or frequency domain. Here, we explore a time-domain based approach. Further, these time-domainbased approaches are broadly classified as equationerror, output error, and filter error [1]; these differin the assumptions considered, their complexity andapplicability. In the approach presented, we utilizeoutput error method (which is based on maximumlikelihood estimation) for estimation of systematicsensor errors, and equation error (which is basedon regression analysis) for estimation of the aero-dynamic parameters.

By assuming a global model for the aerodynamicforces, the problem of system identification canbe reduced to that of parameter estimation. Theknowledge of aerodynamic model structure, thoughnot imperative, if available greatly aids the param-eter estimation process. The steps of the systemidentification are now briefly described.

1) Design of Flight Test: Gathering flight data ina proper fashion is critical to success of the param-eter estimation process. To estimate an aerodynamicmodel which is applicable for the entire flight en-velope from a single flight test, the flight trajectoryhas to be predesigned. Chapter 2 of Ref. [1] providesa detailed description of the flight maneuvers to beused for system identification. MAV/UAV flight testshave an advantage while designing them, in that theconstraint of pilot safety is absent. The tests shouldbe carried out in an environment with minimumwind/gust disturbances.

In the current approach of parameter estimation,measurements of α, β, attitude, thrust, accelera-tions, angular rates, airspeed, actuator deflectionsare required, all of which are available using thesensor suite described in Sec. II. As far as possible,raw data should be recorded so that no informationis lost due to onboard processing. The sensor errorcharacteristics should be determined a priori, toassist for better data processing. The knowledge ofaircraft mass (m), geometry and moment of inertiasis assumed to be known for the duration of flighttest.

2) Data Compatibility Check: Once the flightdata is recorded, it is checked for consistencyand accuracy prior to its use for parameter es-timation. This is done using aircraft kinematicequations, which require the IMU measurements(ax, ay, az, p, q, r) as an input and provide the atti-tudes (φ, θ, ψ), wind angles (α, β) and air speed (V )as the output. This reconstructed data is comparedwith the measured data to check for data compatibil-ity. In case the data is not compatible, sensor errorsshould be removed, as described next.

3) Removal of Sensor Errors: The sensor errorscan be broadly classified as systematic (such as,bias, scale factors) and stochastic (such as, elec-tronic noise). Since the measured data is being pro-cessed offline, digital smoothing is used to remove

the random sensor noise. Use of digital smoothing,allows removal of sensor noise without any filterdelay.

The smoothed data is used along with the aircraftkinematic (data compatibility) equations to providean estimate of the systematic sensor errors. Anoutput error formulation is used which estimates thesensor errors based on maximum likelihood estima-tion (Chapter 6 of Ref. [2]). The measurements arethen corrected using the estimates of sensor bias andscale factors.

4) Parameter Estimation: A regression based pa-rameter estimation algorithm is used along with thecorrected sensor data to arrive at the aerodynamicmodel. The dependent variable is the aerodynamiccoefficient (such as CL, CD, or Cm) to be mod-eled. Using their definitions and measured variables,values of the dependent variables are obtained. Forinstance, lift is obtained based on accelerometer,thrust (T ) and α measurements, which is used forobtaining the value of in flight CL (Eq. 2).

L = (max − T ) sin(α)− (maz) cos(α) (2a)

CL =L

0.5ρV 2S(2b)

The regressors are measured, independent variableswhich are selected based on the physical understand-ing of the aerodynamic models. For instance, α,q, and δe may be used as one set of possible re-gressors while estimating CL model. The parameterestimation algorithm provides an algebraic model ofdependent variable as a function of the regressors.

5) Model Validation: In order to verify the modelthus determined, flights with different trajectoriesshould be flown, and the calculated CL should becompared with the model predicted CL. For a cor-rectly predicted model, the residual, between mea-sured and predicted values, should satisfy a Gausiandistribution with zero mean . The standard deviationquantifies the accuracy of the model, and additionalflight tests should be carried out in case the observeddeviation is not within acceptable limits. Next, weobserve the performance of the parameter estimationmethodology described above, through a six degreeof freedom MAV simulation.

B. Simulation Details

MAV flight simulations have been carried outin order to verify the parameter estimation methoddescribed above. An in-house code for MAV flightsimulation has been used to generate the true tra-jectory and the corresponding measurements. Com-mensurate measurement errors, as shown in Table IIhave been included to simulate the measurements asavailable during a flight. The parameter estimationcodes have been adapted from the SIDPAC softwarepackage [15], and modified for our use.

TABLE IISENSOR ERRORS FOR FLIGHT SIMULATION

Quantity Bias Random Scale(Symbol) Error (1 σ) Factor

Airspeed (V) 0 0.02 m/s 0Wind Angles (α, β) 0 0.3deg 0.5%Angular Rates (p,q,r) 0.25 deg 0.03 deg 0

Acceleration (a) 0.02 g 0.008 g 0Attitude (φ, θ, ψ) 0 0.05 deg 0

In order to simulate the aircraft motion, trueaerodynamic models have to be specified. The trueaerodynamic models influencing the longitudinalmotion are described in Eq. 3. Note that these mod-els are not available with the parameter estimationalgorithm, and are used only for flight simulation.Though, the CL and Cm models used here arelinear, the parameter estimation algorithm is genericin nature and is equally applicable for nonlinearaerodynamic models.

CL = CL0+ CLα

α+ CLδδ (3a)

CD = CD0+ kC2

L (3b)Cm = Cm0

+ Cmαα+ Cmδ

δ (3c)

As mentioned earlier, the choice of flight manou-ver is critical for successful parameter estimation.The aircraft is trimmed at the start of the flight, andthen a pulsed elevator input is provided to excitethe longitudinal dynamic mode. Fig. 12 describesthe elevator (δe) and angle of attack (α) profile forthe flight test.

C. Results

A data compatibility check is first performedusing the noisy measurements obtained from the

Fig. 12. Elevator and α Profile

simulated data. Data compatibility results of Fig.13, indicate that there is a need for sensor errorestimation. First, random errors are eliminated usingdigital smoothing, and this data is used to estimatesensor bias and scale factor errors. Table III showsthe obtained estimates of systematic sensor errors.Note that only measurements affecting the longitu-dinal motion are considered.

Fig. 13. Data Compatibility Analysis

Using the corrected sensor parameters, the aero-dynamic model parameters are being estimated viaregression analysis. Table IV indicates the perfor-mance of the parameter estimation algorithm, bycomparing the true and estimated aerodynamic co-efficients.

TABLE IIISENSOR ERROR ESTIMATION

Parameter True Estimated EstimationError

Accl. Bias (x) 0.1962 0.2189 -0.0227Accl. Bias (z) 0.1962 0.2026 -0.0064Gyro Bias (q) 0.25 0.1949 0.0551α Scale factor 0.005 0.065 -0.06θ Scale factor 0.000 0.002 -0.002

Both the estimates of systematic sensor errors aswell as that of aerodynamic stability derivatives aresatisfactory. However, the algorithm shows signifi-cant error in the estimation of control derivatives.Additional flight maneuvers can be carried out toincrease the accuracy of these estimates.

TABLE IVAERODYNAMIC PARAMETER ESTIMATION

Parameter TRUE Estimated ErrorCL0 +0.1784 +0.1641 +0.0143CLα +2.453 +2.5325 -0.0795CLδ

+0.7405 +1.0437 -0.3032CD0 +0.0871 +0.0942 -0.0071k +0.4 +0.3752 +0.0248

Cm0 +0.0385 +0.0386 -0.0001Cmα -0.5998 -0.5751 -0.0247Cmδ +1.5894 +1.4301 +0.1593

IV. CONCLUSION AND FUTURE WORK

A comprehensive sensor suite for flight testsof mini unmanned aerial vehicles has been de-signed and presented. This sensor suite is capableof measuring all the required variables for systemidentification of MAVs. An elementary algorithmfor estimation of aerodynamic coefficients has alsobeen analyzed, and its simulated results have beenpresented. The algorithm is observed to be workingsatisfactorily for simulated data.

Future work would include improvements in thesensor suite, in terms of optimizing its weight andsize, so as to minimize the effect of sensors onMAV performance. Specifically, the α and β vanesconstruction is to be refined to make them moresleek, small and light. Smaller airspeed and alti-tude measurement sensors are now commerciallyavailable and will further help in miniaturizing theautopilot board. The thrust measurement system is tobe modified for simpler construction and assembly.

Further, detailed error characteristics of the sensorswill also be determined so as to assist in designof on-board estimation and control algorithms aswell as system identification. The current systemidentification algorithm will be validated using realflight data, and required improvements would bemade. Alternative system identification algorithmswhich work even in absence of α, β measurementscould also be investigated.

ACKNOWLEDGMENTS

The authors would like to extend their gratitudeto Aeronautics Research and Development Board(AR&DB) for their constant support of this research.The authors would like to acknowledge the supportof the members of Dynamics and Control Group,Department of Aerospace Engineering, IIT Bom-bay. Continued discussions and timely help fromPrasanna Shevare are highly appreciated.

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