Multi-agent quadrotor testbed control design: Integral Sliding Mode vs. Reinforcement Learning

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Volumn , Issue , 2005, Pages 3712-3717

  Waslander, Steven L ^a   Hoffmann, Gabriel M ^a   Jang, Jung Soon ^a   Tomlin, Claire J ^a  

^a 496 Lomita Mall   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR CUSHION VEHICLES; ALTITUDE CONTROL; AUTONOMOUS AGENTS; DESIGN; GROUND EFFECT; INTELLIGENT ROBOTS; MACHINE LEARNING; MULTI AGENT SYSTEMS; SLIDING MODE CONTROL; TESTBEDS;

AUTONOMOUS ROTORCRAFTS; CLASSICAL CONTROL TECHNIQUES; INTEGRAL SLIDING MODE; LINEAR TECHNIQUES; MULTI-VEHICLE TESTBEDS; MULTIAGENT CONTROL; NONLINEAR DISTURBANCE; REINFORCEMENT LEARNING CONTROL;

REINFORCEMENT LEARNING;

EID: 79957982726     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2005.1545025     Document Type: Conference Paper

References (17)

1. Hoffmann, G., Rajnarayan, D. G., Waslander, S. L., Dostal, D., Jang, J. S., and Tomlin, C. J., "The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC)," 23rd Digital Avionics System Conference, Salt Lake City, UT, November 2004.
2. Lambermont, P., Helicopters and Autogyros of the World, 1958.
3. DraganFly-Innovations, "www.rctoys.com," 2003.
4. Pounds, P., Mahony, R., Hynes, P., and Roberts, J., "Design of a Four-Rotor Aerial Robot," Australian Conference on Robotics and Automation, Auckland, November 2002.
5. Altug, E., Ostrowski, J. P., and Taylor, C. J., "Quadrotor Control Using Dual Camera Visual Feedback," ICRA, Taipei, September 2003.
6. Bouabdallah, S., Murrieri, P., and Siegwart, R., "Design and Control of an Indoor Micro Quadrotor," ICRA, New Orleans, April 2004.
7. Castillo, P., Dzul, A., and Lozano, R., "Real-Time Stabilization and Tracking of a Four-Rotor Mini Rotorcraft," IEEE Transactions on Control Systems Technology, Vol. 12, No. 4, 2004.
8. Bramwell, A., Done, G., and Blamford, D., Bramwell's Helicopter Dynamics, Butterworth-Heinemann, 2nd ed., 2001.
9. Devantech, "http://www.robot-electronics.co.uk/htm/srf08tech.shtml, " SRF08 Ultrasonic Ranger.
10. Ütkin, V., Guldner, J., and Shi, J., Sliding Mode Control in Electromechanical Systems, Taylor-Francis Inc., 1999.
11. Khalil, H. K., Nonlinear Systems, Prentice Hall, 1996.
12. Jang, J. S., Nonlinear Control Using Discrete-Time Dynamic Inversion Under Input Saturation: Theory and Experiment on the Stanford DragonFly UAVs, Ph.D. thesis, Stanford University, 2004.
13. Sutton, R. S. and Barto, A. G., Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
14. Doya, K., Samejima, K., ichi Katagiri, K., and Kawato, M., "Multiple Model-based Reinforcement Learning," Tech. rep., Kawato Dynamic Brain Project Technical Report, KDB-TR-08, Japan Science and Technology Corporatio, June 2000.
15. Ng, A. Y. and Jordan, M. I., "PEGASUS: A policy search method for large MDPs and POMDPs," Uncertainty in Artificial Intelligence, 2000.
16. Ng, A. Y., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., and Liang, E., "Autonomous inverted helicopter flight via reinforcement learning," International Symposium on Experimental Robotics, 2004.
17. Atkeson, C. G., Moore, A. W., and Schaal, S., "Locally Weighted Learning," Artificial Intelligence Review, Vol. 11, No. 1-5, 1997, pp. 11-73. (Pubitemid 127508233)