An optimal control approach to mode generation in hybrid systems

Nonlinear Analysis, Theory, Methods and Applications

Volumn 65, Issue 5, 2006, Pages 963-983

  Mehta, Tejas R ^a   Egerstedt, Magnus ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HYBRID COMPUTERS; LEARNING SYSTEMS; MOTION PLANNING; PROBLEM SOLVING; SYSTEMS ANALYSIS;

CONTINUOUS TIME CONTROL SYSTEMS; CONTROL PROGRAMS; MOTION ALPHABETS; RECURRING MODES;

OPTIMAL CONTROL SYSTEMS;

EID: 33744775347     PISSN: 0362546X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.na.2005.07.044     Document Type: Article

References (21)

1. Arkin R.C. Behavior Based Robotics (1998), The MIT Press, Cambridge, MA
2. Armijo L. Minimization of functions having Lipschitz continuous first-partial derivatives. Pacific Journal of Mathematics 16 (1966) 1-3
3. S.J. Bradtke, B.E. Ydstie, A.G. Barto, Adaptive linear quadratic control using policy iteration, in: American Control Conference, 1994, pp. 3475-3479
4. R.W. Brockett, On the computer control of movement, in: Proceedings of the 1988 IEEE Conference on Robotics and Automation, April 1988, New York, pp. 534-540
5. L. Crawford, S.S. Sastry, Learning controllers for complex behavioral systems, in: Neural Information Processing Systems Tenth Annual Conference, NIPS 96, 1996
6. Egerstedt M. On the specification complexity of linguistic control procedures. International Journal of Hybrid Systems 2 1-2 (2002) 129-140
7. M. Egerstedt, D. Hristu-Varsakelis, Observability and policy optimization for mobile robots, in: IEEE Conference on Decision and Control, Las Vegas, NV, December 2002
8. Egerstedt M., and Brockett R.W. Feedback can reduce the specification complexity of motor programs. IEEE Transactions on Automatic Control 48 2 (2003) 213-223
9. T.T. Georgiou, Relative entropy and the multi-variable multi-dimesional moment problem, IEEE Transaction on IT CLN 04-326. Revised December 2004 (submitted for publication)
10. D. Hristu-Varsakelis, M. Egerstedt, P.S. Krishnaprasad, On the structural complexity of the motion description language MDLe, in: IEEE Conference on Decision and Control, December 2003
11. Jaakkola T., Jordan M.I., and Singh S.P. On the convergence of stochastic iterative dynamic programming algorithms. Neural Computation 6 6 (1994)
12. Kaebling L.P., Littman M.L., and Moore A.W. Reinforcement learning: A survey. Journal of Artificial Intelligence Research (1996)
13. Lygeros J., Tomlin C., and Sastry S. Controllers for reachability specifications for hybrid systems. Automatica 35 3 (1999) 349-370
14. K. Morgansen, R.W. Brockett, Optimal regulation and reinforcement learning for the nonholonomic integrator, in: Proceedings of the American Control Conference, June 2000, pp. 462-466
15. Pappas G.J. Bisimilar linear systems. Automatica 39 12 (2003) 2035-2047
16. Rossmann W. Lie Groups: An Introduction Through Linear Groups (2002), Oxford University Press (Chapter 1.3)
17. Sutton R.S. Generalization in reinforcement learning: Successful examples using sparse coarse coding. Neural Information Processing Systems 8 (1996)
18. Sutton R.S., and Barto A.G. Reinforcement Learning, An Introduction (1998), MIT Press, Cambridge, MA
19. R.C. Thompson, Lecture 10 : Part I - Convergence domains of the Campbell Baker Hausdorff formula, in: John Hopkins Lecture Notes, 1988
20. Tsitsiklis J.N. Asynchronous stochastic approximation and Q-learning. Machine Learning 16 3 (1994)
21. Watkins C.J.C.H., and Dayan P. Q-learning. Machine Learning 8 3-4 (1992) 257-277