1. System Identification of Rotorcraft Rebecca Creed, Mechanical Engineering, University of Dayton Andrea Gillis, Aerospace Engineering, University of Cincinnati Urvish Patel, EE-CompE Accend, University of Cincinnati Dr. Kelly Cohen, Faculty Mentor, University of Cincinnati Mr. Wei Wei, Graduate Mentor, University of Cincinnati June 14, 2013 Part of NSF Type 1 STEP Grant, Grant ID No.: DUE-0756921 1

2. 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. 2

3. 3 Image courtesy of CNN Oklahoma Fire Fighters Rescue Tornado Victims from Rubble Date: May 22, 2013 Rescued: 101 people Lives Lost: 24 people Length 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

4. 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. 4 Image courtesy of csmonitor.com

5. 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 5

6. 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 6

7. 7 System Identification Flowchart Flight Test Process Data CIFER Transfer Function Validation Is the system stable? Yes No Refine the system System Identified!

8. Flight Test Inputs will be given to the rotorcraft Outputs will be recorded 8

9. Process Data 9 Sensor stick used in Rotorcraft – 9DOF Accelerometer ADXL345 Noisy Data Picture from: www.sparkfun.com Filtered DataFilterNext Step

10. 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 10 Kalaman Predictor Equation Measurement Update Equation

11. Result from Kalman Filter 11

12. Moving average Almost same result as kalman filter for our system. Only disadvantage is it takes sometime to start averaging data. 12

13. Results of Moving average 13

14. Moving average and Kalman 14

15. 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 15

16. Flight Training Flight Simulator o Learned how to use controls o The team practiced using this first AR Parrot Drone o Used because of its durability o Emergency landing capabilities Importance o Ensure accurate results o Inputs need to be purposeful to receive clear outputs

17. 17 How the quad-rotor works Take-off/Landing o Increase thrust to move upward o Decrease thrust to move downward Hover - constant heading o Maintain constant thrust o 1 and 3 rotate counter-clockwise o 2 and 4 rotate clockwise

18. How the quad-rotor works Yaw Control – spin cw/counter-cw o 1 and 3 speed up or 2 and 4 slow down o Reverse for other direction Pitch Control – move forward/backward o 4 rotates faster while 2 stays the same or 2 moves slower o Reverse for other direction Roll Control – move right/left o 3 moves faster while 1 stays the same or 1 slows down o Reverse for other direction 18 Pitch Roll Yaw

19. 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/

20. Timeline 20

21. 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, (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 21

22. 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. 22

23. Questions? 23