Highly Efficient Robot Dynamics Learning by Decomposed Connectionist Feedforward Control Structure

IEEE Transactions on Systems, Man, and Cybernetics

Volumn 25, Issue 1, 1995, Pages 145-158

  Katić, Duško M ^a   Vukobratović, Miomir K ^a  

^a MIHAJLO PUPIN INSTITUTE   (Serbia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CONTROL EQUIPMENT; FEEDBACK CONTROL; FEEDFORWARD NEURAL NETWORKS; HIERARCHICAL SYSTEMS; KALMAN FILTERING; LEARNING ALGORITHMS; LEAST SQUARES APPROXIMATIONS; ROBUSTNESS (CONTROL SYSTEMS);

DECOMPOSED CONNECTIONIST FEEDFORWARD CONTROL STRUCTURE; ROBOTS DYNAMIC LEARNING;

ROBOT LEARNING;

EID: 0029209526     PISSN: 00189472     EISSN: 21682909     Source Type: Journal    
DOI: 10.1109/21.362958     Document Type: Article

References (37)

1. G. N. Saridis, “Intelligent robotic control,” IEEE Transactions on Automatic Control vol. AC-28. no. 5, pp. 547–557, 1983.
2. A. Meystel “Intelligent control in robotics,” Journal of Robotic Systems, vol. 5, no. 4, pp. 269–308, 1988.
3. D. Katić and M. Vukobratović, “Decomposed connectionist architecture for fast and robust learning of robot dynamics,” in Proceedings of the IEEE International Conference on Robotics and Automation, (Nice), pp. 2064–2069, May, 1992.
4. S. Y. Kung and J.-N. Hwang, “Neural network architectures for robotic applications,” IEEE Transactions on Robotics and Automation, vol. 5, no. 5, pp. 641–657, 1989.
5. D. Psaltis, A. Sideris and A. Yamamura, “A hierarchical model for voluntary movement and its application to robotics,” in Proceedings of the 1st IEEE International Conference on Neural Networks San Diego, pp. 551–558, June, 1987.
6. W. T. Miller, R. P. Hewes, F. H. Glanz and L. G. Kraft, “Real-time dynamic control of an industrial manipulator using a neural-network-based learning controller,” IEEE Transactions on Robotics and Automation, vol. 6, no. 1, pp. 1–9, 1990.
7. A. Guez, J. Eilbert and M. Kam, “Neuromorphic architectures for fast adaptive robot control,” in Proceedings of the IEEE International Conference on Robotics and Automation, (Raleigh), pp. 145–149, April 1988.
8. D. F. Bassi, “Connectionist Dynamic Control of Robot Manipulators,” Ph.D. University of Southern California, Los Angeles, December 1990.
9. D. Y. Yeung and G. A. Bekey, “Using a context-sensitive learning network for robot arm control,” in Proceedings of the IEEE International Conference on Robotics and Automation, (Scottsdale), pp. 1441–1447, May, 1989.
10. M. Kawato, K. Furukawa and R. Suzuki, “A hierarchical neural network model for control and learning of voluntary movement,” Bilogical Cybernetics, vol. 57, pp. 169–185, 1987.
11. H. Miyamoto, M. Kawato, T. Setoyama and R. Suzuki, “Feedback-error-learning neural network for trajectory control of a robotic manipulator,” Neural Networks, vol. 1, pp. 251–265, 1988.
12. M. Kawato, Y. Uno, M. Isobe and R. Suzuki, “Hierarchical neural network model for voluntary movement with application to roboticsi,” IEEE Control Systems Magazine, vol. 8., no. 2, pp. 8–16, 1988.
13. D. Katić, “Connectionist learning models for Intelligent control of manipulation robots,” in Proceedings of the IMACS International Symposium on Mathematical and Intelligent Models in System Simulation, (Brussels), pp. I.C.3-1-I.C.3-6, September 1990.
14. K. Goldberg and B. Pearlmutter, “Using a neural network to learn the dynamics of the CMU direct-drive arm II,” Tech. Rep. CMU-CS-88-160, Carnegie Mellon University, Pittsburgh, August 1988.
15. T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma and Y. Uchikawa, “Trajectory control of robotic manipulators,” IEEE Transations on Industrial Electronics, vol. 38, no. 3, pp. 195–202, 1991.
16. J. K. Kruschke and J. R. Movellan, “Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks,” IEEE Transations on Systems, Man and Cybernetics, vol. 21, no. 1, pp. 273–280, 1991.
17. M. Vukobratović, D. Stokić and N. Kirćanski, “Non-adaptive and adaptive control of manipulation robots,” Springer Verlag, Berlin, 1985.
18. S. Arimoto, S. Kawamura and F. Miyazaki, “Bettering operation of robots by learning,” Journal of Robotic Systems, vol. 1, pp. 123–140, 1984.
19. N. Sadegh, R. Horowitz, W.-W. Kao and M. Tomizuka, “A Unified approach to design of adaptive and repetitive controllers for robotic manipulators,” in Proceedings of the USA-Japan Symposium on Flexible Automation, (Minneapolis), pp. 223–231, 1988.
20. C. G. Atkeson and D. J. Reinkensmeyer, “Using associative-addressable memories to control robots,” in Proceedings of the IEEE International Conference on Robotics and Automation, (Scottsdale), pp. 1859–1864, May, 1989.
21. H. Asada, “Teaching and learning of compliance using neural nets: representation and generation of neural compliance,” in Proceedings of the IEEE International Conference on Robotics and Automation, Cincinnati, pp. 1237–1244, May, 1990.
22. H. Liu, T. Iberall and G. A. Bekey, “Neural network architectures for robot hand control,” IEEE Control Systems Magazine, vol. 9, no. 2, pp. 38–43, 1989.
23. D. E. Rumelhart and J. L. McClelland, “Parallel distributed processing (PDP): Exploration in the microstructure of cognition,” vol. 1–2, Cambridge: MIT Press, 1986.
24. D. F. Bassi and G. A. Bekey, “Decomposition of neural networks models of robot dynamics: a feasibility study,” in Simulation and A.I., (W. Webster, ed.), pp. 8–13, The Society for Computer Simulation International, 1989.
25. M. J. Quinn, “Designing efficient algorithms for parallel computers,” New York, McGraw-Hill, New York, 1987.
26. C. J. Wang and C.-H. Wu, “Parallel simulation of neural networks,” Simulation, vol. 56, no. 4, pp. 223–232, 1991.
27. R. Hecht-Nielsen, “Neurocomputing,” Addison-Wesley, Reading: 1990.
28. G. Agha, “Actors, A Model for concurrent Computation in Distributed Systems” Cambridge, The MIT-Press, 1986.
29. T. Tollenaere, “SuperSab:Fast adaptive back propagation with good scaling properties,” Neural Networks, vol. 3, no. 5, pp. 561–573, 1990.
30. R. A. Jacobs, “Increased rates of convergence through learning speed in back-propagation networks,” Neural Networks, vol. 1, no. 2, pp. 295–307, 1988.
31. M. K. Weir, “A method for self-determination of adaptive learning rates in back propagation,” Neural Networks, vol. 4, no. 3, pp. 371–379, 1991.
32. A. K. Rigler, J. M. Irvine and T. P. Vogl, “Rescaling of variables in back propagation learning,” Neural Networks, vol. 4, no. 2, pp. 225–229, 1991.
33. L. Ljung and T. Soderstrom, “Theory and Practice of Recursive Identification.” Cambridge: The MIT-Press, 1983.
34. S. Stefanović and B. Kovačević, “Neural networks learning using recursive least-squares method”, Proceedings of the IASTED Conference on Signal Processing and Digital Filtering, Lugano, 1990, pp. 551–556.
35. K. Watanabe, T. Fukuda and S. G. Tzafestas, “Learning algorithms of layered neural networks via extended kalman filters,” International Journal of Systems Science, vol. 22, no. 4, pp. 753–768, 1991.
36. Y. Iiguni, H. Sakai and H. Tokumaru, “A real-time learning algorithm for a multilayered neural network based on the extended kalman filter,” IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 959–966, 1992.
37. S. Turk and M. Otter, “The DFVLR Models 1 and 2 of the Manutec r3 robot,” Robotersysteme, no. 3, pp. 753–768, 1987 (in German).