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 May 31, 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 9 sensors have been placed on the rotorcraft These sensors will measure the outputs The outputs can then be compared with the inputs to see how the rotorcraft reacts

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

9. 9 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 2 and 4 rotate counter-clockwise o 1 and 3 rotate clockwise

10. 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 10 PitchRoll Yaw

11. Process Data 11 Sensor stick used in Rotorcraft – 9DOF Accelerometer ADXL345 Noisy Data Picture from: www.sparkfun.com Filtered Data Kalman Filter Next Step

12. 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

13. Timeline 13

14. 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 14

15. 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. 15

16. Questions? 16