A neighboring optimal adaptive critic for missile guidance

Mathematical and Computer Modelling

Volumn 23, Issue 1-2, 1996, Pages 175-188

  Dalton, J ^a   Balakrishnan, S N ^b  

^a Center for Intelligent Systems   (United States)

^b Architectural and Environmental Engineering   (United States)

Author keywords

Adaptive critic; Neural networks; Neuro control; Optimal guidance; Reinforcement learning

Indexed keywords

EID: 0001192446     PISSN: 08957177     EISSN: None     Source Type: Journal    
DOI: 10.1016/0895-7177(95)00226-X     Document Type: Article

References (28)

1. H. Pastrick, S. Seltzer and M. Warren, Guidance laws for short-range tactical missile, Journal of Guidance and Control 4 (March/April), 98-108 (1981).
2. F.W. Nesline and P. Zarchan, A new look at classical vs. modern homing missile guidance, Journal of Guidance and Control 4 (January/February), 78-85 (1981).
3. A.E. Bryson, Jr. and Y. Ho, Applied Optimal Control, Hemisphere Publishing Corporation, (1975).
4. P. Zarchan, Tactical and Strategic Missile Guidance, Vol. 124 of Progress in Astronautics and Aeronautics, American Institute of Aeronautics and Astronautics, Inc., (1990).
5. P.H. Fiske, Advanced digital guidance and control concepts for air-to-air tactical missiles, Tech. Rep. FAFTL-TR-77-130, Eglin AFB, FL, (1977).
6. S.N. Balakrishnan and J.L. Speyer, Assumed density filter with application to homing missile guidance, AIAA Journal of Guidance, Control and Dynamics (January/February), 4-12 (1989).
7. S.N. Balakrishnan, An extension to modified polar coordinates and application with passive measurements, AIAA Journal of Guidance, Control and Dynamics (November/December), 906-912 (1989).
8. S.N. Balakrishnan and R. Weil, Neurocontrol: A literature survey, Mathl. Comput. Modelling (this issue).
9. J. Albus, A new approach to manipulator control: The Cerebellar Model Articulation Controller (CMAC), Transactions of the ASME, 220-227 (September 1975).
10. J.A. Franklin and O.G. Selfridge, Some new directions for adaptive control theory in robotics, In Neural Networks for Control, (Edited by W. Miller II, R. Sutton and P. Werbos), Chapter 14, pp. 349-364, MIT Press, (1990).
11. M. Kawato, Computational schemes and neural network models for formation and control of multijoint arm trajectory, In Neural Networks for Control, (Edited by W. Miller II, R. Sutton and P. Werbos), Chapter 9, pp. 197-228, MIT Press, (1990).
12. K. Tsutsumi, K. Katayama and H. Matsumoto, Neural computation for controlling the configuration of 2-dimensional truss structure, In Proceedings of the IEEE International Conference on Neural Networks, San Diego, CA, July 24-27, 1987, Vol. 2.
13. G. Wang and D. Miu, Unsupervised adaptive neural-network control of complex mechanical systems, In Proceedings of the 1991 American Control Conference, Boston, MA, June 26-28, 1991, pp. 28-31.
14. th Conference on Decision and Control, Tampa, FL, December 1989, pp. 260-265.
15. J. Steck and S.N. Balakrishnan, Use of hopfield neural networks in optimal guidance, IEEE Trans. on Aerospace and Electronic Systems January, 287-293 (1994).
16. D. Rumelhart, G. Hinton and R. Williams, Learning internal representations by error propagation, In Parallel Distributed Processing: Explorations in the Micro structure of Cognition, (Edited by D. Rumelhart, J. McClelland and P.R. Group), MIT Press, (1986).
17. J.J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, In Proceedings National Academy of Science, Vol. 79, pp. 2554-2558, (April 1982).
18. J.J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons, In Proceedings National Academy of Science, Vol. 81, pp. 3088-3092, (May 1984).
19. K.S. Fu, Learning control systems, In Computer and Information Sciences, (Edited by J.T. Tou and R.H. Wilcox), pp. 318-343, Spartan Books, Washington, DC, (1964).
20. J. Sklansky, Learning systems for automatic control, IEEE Transactions on Automatic Control AC-11, 6-19 (January 1966).
21. Z. Nikolic and K. Fu, An algorithm for learning without external supervision and its application to learning control systems, IEEE Transactions on Automatic Control AC-11, 414-422 (July 1966).
22. J. Mendel and R. McLaren, Reinforcement learning control and pattern recognition systems, In Adaptive, Learning, and Pattern Recognition Systems: Theory and Applications, (Edited by J. Mendel and K.S. Fu), pp. 287-318, Academic Press, New York, (1970).
23. A.G. Barto, R.S. Sutton, and C.W. Anderson, Neuronlike adaptive elements that can solve difficult learning control problems, IEEE Transactions on Systems, Man, and Cybernetics SMC-13 (September/October), 834-846 (1983).
24. P. Werbos, Backpropagation and neurocontrol: A review and prospectus, In Proceedings of the International Joint Conference on Neural Networks, Washington, DC, June 18-22, 1989, Vol. I, pp. 209-216.
25. P. Werbos, Approximate dynamic programming for real-time control and neural modeling, In Handbook of Intelligent Control, (Edited by D. White and D. Sofge), Chapter 13, pp. 493-525, Van Nostrand Reinhold, New York, (1992).
26. J. Jameson, A neurocontroller based on model feedback and the adaptive heuristic critic, In Proceedings of the International Joint Conference on Neural Networks, San Diego, June 1990, pp. II37-II44.
27. th Conference on Decision and Control, New York, December 1990, pp. 3270-3276.
28. J.S. Dalton, A critic based system for neural guidance and control, Ph.D. Thesis, Electrical Engineering, University of Missouri-Rolla, Rolla, MO, (December 1994).