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System Identification of Rotorcraft

Rebecca Creed, Mechanical Engineering, University of DaytonAndrea Gillis, Aerospace Engineering, University of Cincinnati

Urvish Patel, EE-CompE Accend, University of Cincinnati

Dr. Kelly Cohen, Faculty Mentor, University of CincinnatiMr. Wei Wei, Graduate Mentor, University of Cincinnati

June 14, 2013

Part of NSF Type 1 STEP Grant, Grant ID No.: DUE-0756921

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Introduction• Natural disasters take thousands of lives

every year. • Many first responders perform dangerous

rescue missions to save lives.• Technology will allow first responders to

assess the situation more quickly and efficiently.

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Image courtesy of CNN

Oklahoma Fire Fighters Rescue Tornado Victims from Rubble

Date: May 22, 2013Rescued: 101 peopleLives Lost: 24 peopleLength of Search: ~ 48 hrs

• Rotorcrafts equipped with heat sensors and cameras will reduce the length of searches.

• Shorter searches will hopefully reduce the lives lost.

2013 Oklahoma Tornado

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2012 Colorado Wildfire• The progression of the fire could not be

anticipated.• Once the fire had become an issue, the best

way to access it was unknown.• An autopilot equipped rotorcraft would be able to use a camera and assess the situation.

Image courtesy of csmonitor.com

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Why Autopilot?• Easy to use with simple controls• Increase the range of the rotorcraft– Without autopilot, the rotorcraft must remain in

the operator’s line of sight• A dynamic model is necessary to develop an

autopilot

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System Identification• A dynamic model is a representation of the

behavior of a system (for this case, rotorcraft)• Two options for creating a dynamic model– System Identification– Wind Tunnel Testing

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System Identification FlowchartFlight Test

Process Data

CIFER

Transfer Function

Validation

Is the system stable?

Yes No

Refine the system

System Identified!

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Flight Test• Inputs will be given to the rotorcraft• Outputs will be recorded

Process Data

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Sensor stick used in

Rotorcraft – 9DOF

Accelerometer ADXL345

Noisy Data

Picture from: www.sparkfun.com

Filtered Data

FilterNext Step

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Kalman Filter for Linear System• x = x• p = p + q;• k = p / (p + r);• x = x + k * (measured – x);• p = (1 – k) * p;

x = filtered value p = estimated error q = processed noice r = Sensor Noise k = kalman gain

Kalaman Predictor Equation

Measurement Update Equation

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Result from Kalman Filter

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

X axis

RegularKalman

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Moving average• Almost same result as kalman filter for our

system. • Only disadvantage is it takes sometime to

start averaging data.

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Results of Moving average

-6

-4

-2

0

2

4

6

X axis

RegularMoving average

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Moving average and Kalman

-2

-1.5

-1

-0.5

0

0.5

1

1.5

X axis

RegularKalmanMoving average

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CIFER• CIFER is used to obtain a transfer function from our inputs

and outputs• CIFER is a sophisticated tool for system identification• Developed by US Army and UC Santa Cruz

Flight Training• Flight Simulator

o Learned how to use controlso The team practiced using this first

• AR Parrot Droneo Used because of its durabilityo Emergency landing capabilities

• Importanceo Ensure accurate resultso Inputs need to be purposeful

to receive clear outputs

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How the quad-rotor works

• Take-off/Landingo Increase thrust to move upwardo Decrease thrust to move downward

• Hover - constant headingo Maintain constant thrusto 1 and 3 rotate counter-clockwiseo 2 and 4 rotate clockwise

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How the quad-rotor works• Yaw Control – spin cw/counter-cw

o 1 and 3 speed up or 2 and 4 slow downo Reverse for other direction

• Pitch Control – move forward/backwardo 4 rotates faster while 2 stays the same or 2 moves slowero Reverse for other direction

• Roll Control – move right/lefto 3 moves faster while 1 stays the same or 1 slows downo Reverse for other direction

Pitch RollYaw

UAV Advantages• Maneuverability• Cost• Endurance• Safer for Crews• Size• Indoor Flight• Sushi Delivery

Image courtesy of http://www.todaysiphone.com/2013/06/yo-sushi-delivering-food-on-ipad-controlled-trays/

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TimelineWeek 1 2 3 4 5 6 7 8

Literature and technical Review

Learn how to fly AR Drone

Flight testing

Data Processing

System Identification

Document Findings

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References• Bestaoui, Y., and Slim, R. (2007). “Maneuvers for a Quad-Rotor Autonomous Helicopter,” AIAA Infotech@Aerospace Conference, held

at Rohnert Park, California, May 7-10, pp.1-18

• Chen, M., and Huzmezan, M. (2003). “A Combined MBPC/2 DOF H∞ Controller for a Quad Rotor UAV,” AIAA Guidance, Navigation,

and Control Conference and Exhibit, held at Austin, Texas, August 11-14, n.p.

• Esme, B. (2009). “Kalman Filter For Dummies.” Biligin’s Blog, <http://bilgin.esme.org/BitsBytes/KalmanFilterforDummies.aspx> (Mar.

2009).

• Guo, W., and Horn, J. (2006). “Modeling and Simulation For the Development of a Quad-Rotor UAV Capable of Indoor Flight ,” AIAA

Modeling and Simulation Technologies Conference, held at Keystone, Colorado, August 21-24, pp.1-11

• Halaas, D., Bieniawski, S., Pigg, P., and Vian, J. (2009). “Control and Management of an Indoor Health Enabled, Heterogenous Fleet,”

AIAA Infotech@Aerospace Conference, held at Seattle, Washington, April 6-9, pp.1-19

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References• Koehl, A., Rafaralahy, H., Martinez, B., and Boutayeb, M. (2010). “Modeling and Identification of a Launched Micro Air Vehicle: Design and

Experimental Results,” AIAA Modeling and Simulation Technologies Conference, held at Toronto, Ontario Canada, August 2-5, pp.1-18

• Mehra, R., Prasanth, R., Bennett, R., Neckels, D., and Wasikowski, M. (2001). “Model Predictive Control Design for XV-15 Tilt Rotor Flight

Control,” AIAA Guidance, Navigation, and Control Conference and Exhibit, held at Montreal, Canada, August 6-9, pp. 1-11.

• Milhim, A., and Zhang, Y. (2010). “Quad-Rotor UAV: High-Fidelity Modeling and Nonlinear PID Control,” AIAA Modeling and Simulation

Technologies Conference, held at Toronto, Ontario, Canada, August 2-5, pp. 1-10.

• Salih, A., Moghavvemi, M., Mohamed, H., and Gaeid, K. (2010). “Flight PID controller design for a UAV quadrotor,” Scientific Research and

Essays, ????, Vol. 5, No. 23, pp. 3660-3667.

• Tischler, M.B., and Cauffman, M.G. (2013). “Frequency-Response Method for Rotorcraft System Identification: Flight Applications to BO-105

Coupled Fuselage/Rotor Dynamics,” University Affiliated Research Center: A Partnership Between UCSC and NASA Ames Research Center,

pp. 1-13.

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Questions?