Neurocontroller using dynamic state feedback for compensatory control

Neural Networks

Volumn 10, Issue 9, 1997, Pages 1691-1708

  Szepesvári, Csaba ^b   Cimmer, Szabolcs ^b   Lorincz, András ^a  

^a INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

^b NONE

Author keywords

Compensating perturbations; Feedback control; Feedforward control; Inverse dynamics; Liapunov's second method; Neural network control; On line learning; Stability

Indexed keywords

COMPUTER SIMULATION; FEEDBACK CONTROL; LEARNING SYSTEMS; MATHEMATICAL MODELS; ONLINE SYSTEMS; PERTURBATION TECHNIQUES; SYSTEM STABILITY;

COMPENSATORY CONTROL; DYNAMIC STATE FEEDBACK; LIAPUNOV SECOND METHOD; NEURAL NETWORK CONTROL;

NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; FEEDBACK SYSTEM; INFORMATION SYSTEM; PRIORITY JOURNAL; ROBOTICS;

EID: 0031455549     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(97)00043-9     Document Type: Article

References (48)

1. Albus, J. (1971). A theory of cerebellar function. Mathematical Biosciences, 10, 25-61.
2. Anderson, C., & Miller, W. I. III (1992). Challenging control problems. In Miller III, W. T., Sutton, R., & Werbos, P. J. (eds) Neural networks for control series. Neural network modeling and connectionism (pp. 475-510). Cambridge: MIT Press.
3. Ben-Israel, A., & Greville, T. (1974). Generalized inverses: theory and applications. In Pure and applied mathematics. New York: Wiley.
4. Connolly, C., & Grupen, R. (1993). On the application of harmonic functions to robotics. Journal of Robotic Systems, 10(7), 931-946.
5. Dean, T., & Wellman, M. (1991). Planning and control. San Mateo, CA: Morgan Kaufmann.
6. Fomin, T., Szepesvári, C., & Lorincz, A. (1994). Self-organizing neurocontrol. In Proceedings of IEEE WCCI ICNN'94, Vol. 5 (pp. 2777-2780). Orlando: IEEE.
7. Glasius, R., Komoda, A., & Gielen, S. (1995). Neural network dynamics for path planning and obstacle avoidance. Neural Networks, 8(1), 125-133.
8. Goodwin, G., & Sin, K. (1984). Adaptive filtering, prediction and control. Englewood Cliffs, NJ: Prentice-Hall.
9. Grossberg, S., & Kuperstein, M. (1986). Neural dynamics of adaptive sensory-motor control: ballistic eye movements. Amsterdam: Elsevier.
10. Hwang, Y., & Ahuja, N. (1992). Gross motion planning - a survey. ACM Computing Surveys, 24(3), 219-291.
11. Isidori, A. (1989). Nonlinear control systems. Berlin: Springer.
12. Jordan, M. (1990). Attention and performance, XIII. Hillsdale, NJ: Erlbaum.
13. Kawato, M., Furukawa, K., & Suzuki, R. (1987). A hierarchical neural-network model for control and learning of voluntary movements. Biological Cybernetics, 57, 169-185.
14. Keymeulen, D., & Decuyper, J. (1992). On the self-organizing properties of topological maps. In P. Bourgine & F. Vanda (eds), Toward a practice of autonomous systems, Proceedings of the First European Conf. on Artificial Life (pp. 64-69). MIT Press Cambridge, MA.
15. La Salle, J., & Lefschetz, S. (1961). Mathematics in science and engineering. New York: Academic Press.
16. Landau, Y. (1979). Adaptive control: the model reference approach. New York: Marcel Dekker.
17. Lei, G. (1990). A neural model with fluid properties for solving labyrinthian puzzle. Biological Cybernetics, 64(1), 61-67.
18. Lewis, F., Abdallah, C., & Dawson, D. (1993). Control of robot manipulators. New York: MacMillan.
19. Lewis, F., Liu, K., & Yesildirek, A. (1995). Neural net robot controller with guaranteed tracking performance. IEEE Transactions on Neural Networks, 6(3), 703-715.
20. Locano-Pèrez, T., & Wesley, M. (1979). An algorithm for planning collision-free paths among polyhedral objects. Communications of ACM, 22(10), 560-570.
21. Lovelock, D., & Rund, H. (1975). Tensors, differential forms, and variational principles. In Pure and Applied Mathematics. New York: Wiley.
22. Marr, D. (1969). A theory of cerebellar cortex. Journal of Physiology (London), 202, 437-470 [reprinted in Vaina, L. (Ed.) (1991) From retina to the neocortex: selected papers of David Marr (pp. 129-203). Boston: Birkhauser].
23. Marr, D. (1969). A theory of cerebellar cortex. Journal of Physiology (London), 202, 437-470 [reprinted in Vaina, L. (Ed.) (1991) From retina to the neocortex: selected papers of David Marr (pp. 129-203). Boston: Birkhauser].
24. Miller III, W. T. (1987). Sensor based control of robotic manipulators using a general learning algorithm. IEEE Journal of Robotics and Automation, 3, 157-165.
25. Miller, W. I., Sutton, R., & Werbos, P. (Eds.) (1990). Neural networks for control. Cambridge, MA: MIT Press.
26. Minsky, M. (1961). Steps towards artificial intelligence. In Proceedings of the Institute of Radio Engineers (pp. 8-30) [reprinted in (1963), E. A. Feigenbaum & J. Feldman (Eds.), Computers and thought (pp. 406-450). New York: McGraw-Hill].
27. Minsky, M. (1961). Steps towards artificial intelligence. In Proceedings of the Institute of Radio Engineers (pp. 8-30) [reprinted in (1963), E. A. Feigenbaum & J. Feldman (Eds.), Computers and thought (pp. 406-450). New York: McGraw-Hill].
28. Miyamoto, H., Kawato, M., Setoyama, T., & Suzuki, R. (1988). Feedback-error-learning neural network for trajectory control of a robotic manipulator. Neural Networks, 1, 251-265.
29. Morasso, P., Sanguineti, V., & Tsuji, T. (1993). Neural network architecture for robot planning. In M. Marinaro and P. G. Morasso (eds) Proceedings of ICANN'93 (pp. 256-261). Amsterdam, London: Springer.
30. Narendra, K., & Monopoli, R. (1980). Applications of adaptive control. New York: Academic Press.
31. Narendra, K., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
32. Ortega, R., & Yu, T. (1987). Theoretical results on robustness of direct adaptive controllers: a survey. In Proceedings of 10th IFAC World Congress (pp. 26-31) Oxford: Pergamon.
33. Psaltis, D., Sideris, A., & Yamamura, A. (1988). A multilayered neural network controller. IEEE Control Systems Magazine, 8, 17-21.
34. Rumiantsev, V. (1957). On the stability of a motion in a part of variables. University Series I, Mathematical Mechanics, 4, 9-16.
35. Slotine, J.-J., & Li, W. (1991). Applied nonlinear control. Englewood Cliffs, NJ: Prentice-Hall.
36. Szepesvári, C., & Lörincz, A. (1996a). Approximate geometry representation and sensory fusion. Neurocomputing, 12(2-3), 267-287.
37. Szepesvári, C., & Lörincz, A. (1996b). High precision neurocontrol of a chaotic bioreactor. In 2nd World Congress of Nonlinear Analysts (invited talk).
38. Szepesvári, C., & Lörincz, A. (1996c). Neurocontrol I: self-organizing speed-field tracking. Neural Network World, 6, 875-896.
39. Szepesvári, C., & Lörincz, A. (1996d). Neurocontrol II: high precision control achieved using approximate inverse dynamics models. Neural Network World, 6, 897-920.
40. Szepesvári, C., & Lorincz, A. (submitted). Integrated architecture for motion control and path planning. Journal of Robotic Systems.
41. Szepesvári, C., Balázs, L., & Lorincz, A. (1994). Topology learning solved by extended objects: a neural network model. Neural Computation, 6(3), 441-458.
42. Tarassenko, L., & Blake, A. (1991). Analogue computation of collision-free paths. In Proceedings of the 1991 IEEE International Conference on Robotics and Automation (pp. 500-505). IEEE Press, Piscataway, NJ.
43. Ungar, L. (1992). A bioreactor benchmark for adaptive network-based process control. In W. T. Miller, III, R. S. Sutton & P. J. Werbos (eds) Neural networks for control series, neural network modeling and connectionism (pp. 387-402). Cambridge: MIT Press.
44. Vemuri, V. (1993). Artificial neural networks in control applications. Advances in Computers, 36, 203-254.
45. Werbos, P. (1988). Generalization of back propagation with applications to a recurrent gas market model. Neural Networks, 1, 339-356.
46. Widrow, B. (1986). Adaptive inverse control. In Proceedings of the Second IFAC Workshop on Adaptive Systems in Control and Signal Processing (pp. 1-5). Lund, Sweden: Lund Institute of Technology.
47. Widrow, B., McCool, J., & Medoff, B. (1978). Adaptive control by inverse modeling. In 20th Asilomar Conference on Circuits, Systems and Computers.
48. Young, L. (1969). On adaptive manual controls. IEEE Transactions on Man-Machine Systems, 10, 292-331.