Download - System Identification of Rotorcraft
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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 28, 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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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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So, what is System Identification?
SystemInputs Outputs
Given the inputs to a system, a system model can predict the outputs
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Simple Example: Pushing a Sled
Input is the “pushing” force applied to the sled
Output is the sled’s movement
Sled• Push(force)• Acceleration• Velocity• Displacement
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System Inputs and Outputs
• 4 inputs– Yaw– Pitch– Roll– Thrust
• 9 outputs– 3 attitudes– 3 angular rates– 3 accelerations
Aeroquad System
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System Identification FlowchartFlight Testing
Data Processing
Data Evaluation
System Model
Validation
System Identified!
CIFER
MATLAB
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Flight Test• Inputs given to the rotorcraft by controller
• Outputs recorded by the sensor stick (IMU)
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How the quad-rotor worksYaw Control
spin cw/counter-cw
Roll Controlmove right/left
Pitch Controlmove forward/backward
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Data ProcessingFlight Testing
Data Processing
Data Evaluation
System Model
Validation
System Identified!
Record raw data in MATLAB program
Filter recorded data
Reformat data for use in CIFER
Filter 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
• Kalman filter finds the optimum averaging factor for each consequent state and also remembers some information about previous state
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Kalman Filter for Linear System x = filtered value p = estimated error q = processed noise r = Sensor Noise k = Kalman gain
• p = p + q• k = p / (p + r)• x = x + k * (measured – x)• p = (1 – k) * p
Kalman 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• Similar results as Kalman filter for our system• Moving average is less efficient than Kalman
filter
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Results of Moving average
-6
-4
-2
0
2
4
6
X axis
RegularMoving average
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-2
-1.5
-1
-0.5
0
0.5
1
1.5
X axis
RegularKalmanMoving average
Moving average and Kalman
Regular
Kalman
Moving Average
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Data Evaluation – CIFER
CIFER • stands for Comprehensive Identification from
Frequency Responses
• Advanced tool used for System Identification
• Developed by the U.S. Army and the University of California Santa Cruz
• We use CIFER to identify the Aeroquad system
CIFER image from: http://uarc.ucsc.edu/flight-control/cifer/
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Data Evaluation – FRESPID
– First Step:
FRESPID COMPOSITE NAVFIT
Finds the frequency response of our data
Uses windowing to combine FRESPID
results
Finds a transfer function from our
combined frequency response
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• Frequency response relates the inputs and outputs of our data
Input Data
Output Data
Frequency Response
Data Evaluation – FRESPID
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Data Evaluation – COMPOSITE
– Second Step:
FRESPID COMPOSITE NAVFIT
Finds the frequency response of our data
Uses windowing to combine FRESPID
results
Finds a transfer function from our
combined frequency response
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Data Evaluation – COMPOSITE• COMPOSITE combines parts of the frequency
responses that have the best coherence
FRESPID Frequency Response COMPOSITE Frequency Response
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Data Evaluation – NAVFIT
– Last Step:
FRESPID COMPOSITE NAVFIT
Finds the frequency response of our data
Uses windowing to combine FRESPID
results
Finds a transfer function from our
combined frequency response
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Data Evaluation – NAVFIT• NAVFIT fits a transfer function
to the COMPOSITE frequency response
COMPOSITE Frequency Response
Transfer Function Phase and Magnitude
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• CIFER produces transfer functions for three motions• These transfer functions model the Aeroquad system
and must be stable
Transfer Function =
Data Evaluation – Stability
𝐴𝑠+𝐵𝑠2+𝐶𝑠+𝐷
𝑖
𝑠
Poles should be on this side! Stable Example
𝐻 (𝑠 )= 𝑠+2𝑠2+2 𝑠+1
𝑟𝑜𝑜𝑡𝑠 : 𝑠=−1,−1
Negative real roots
Unstable Example
𝐻 (𝑠 )= 𝑠+2𝑠2−𝑠+1
𝑟𝑜𝑜𝑡𝑠 : 𝑠=0.5 ±0.866 𝑖
Positive real roots
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UAV Advantages• Maneuverability• Capable of indoor flight• Safer for Crews• Endurance• Cost• Sushi Delivery
Image courtesy of http://www.todaysiphone.com/2013/06/yo-sushi-delivering-food-on-ipad-controlled-trays/
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Progress
*Plan to submit Journal paper and Conference paper from this research
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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 ProcessingSystem Identification
Paper
Presentation
Poster
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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?