Adaptive control of manipulators with improved adaptation transients by using on-line supervision of the free-parameters of the adaptation algorithm and of the sampling rate

Cybernetics and Systems

Volumn 33, Issue 7, 2002, Pages 749-778

  Sen, M De La ^a   Almansa, A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTER SIMULATION; CONTROL SYSTEM SYNTHESIS; EQUATIONS OF MOTION; MANIPULATORS; NEURAL NETWORKS; PARAMETER ESTIMATION; ROBOT LEARNING; SAMPLING; SCADA SYSTEMS; TORQUE CONTROL; TRANSIENTS;

ADAPTATION ALGORITHM; ADAPTATION TRANSIENTS; ONLINE SUPERVISION; SAMPLING RATE; TRACKING ERROR;

ADAPTIVE CONTROL SYSTEMS;

EID: 0036795721     PISSN: 01969722     EISSN: 10876553     Source Type: Journal    
DOI: 10.1080/01969720213936     Document Type: Article

References (28)

1. Antsakis, P. J. 1990. Neural networks in control systems, IEEE Control Systems Magazine 10:3-5.
2. Barry, P. E. 1975. Optimal control of multirate systems. Report RN-361, Research Department, Grumman Aersopace Corporation, Bethpage NY, USA.
3. Craig, J. J. 1986. Introduction to Robotics, Mechanics and Control. Reading, MA:Addison-Wesley.
4. Craig, J. J. 1988. Adaptive Control of Mechanical Manipulators. Reading, MA:Addison-Wesley.
5. De La Sen, M. 1984a. On-line optimization of the free parameters in discrete adaptive control systems. Proc. IEE-D 31(4):146-157.
6. De La Sen, M. 1984b. A new modeling for aperiodic sampling system. Int. J. of Systems Science 15(3):315-328.
7. De La Sen, M. 1985. Modified adaptive controllers for improving the transients in adaptive control. Computer Aided Design CAD 17(1):9-14.
8. De La Sen, M. 1986. Multirate hybrid adaptive control. IEEE Trans. Automat. Control 17(1):9-14.
9. Hsia, T. C. 1972. Comparisons of adaptive sampling control laws. IEEE Trans. Automat. Contr. AC-17:830-831.
10. Hsia, T. C. 1974. Analytic design of adaptive sampling control laws. IEEE Trans. Automat. Contr. AC-19:39-42.
11. Feng, G., and R. L. Ren. 1995. Robust adaptive control for robot manipulators. International Journal of Systems Science 26:1017-1028.
12. Fuster, A., and J. M. Guillen. 1987. New modeling technique for aperiodic sampling of nonlinear systems. Int. J. of Control 45(3):951 -968.
13. Glasson, D. P. 1980. Research in multirate estimation and control. Report TR 1356/1, The Analytic Science Corporation, Reading, MA, USA.
14. Gupta, M. M., and D. H. Rao. 1994. Neuro-Control Systems. Piscataway, N.J: IEEE Press.
15. Hunt, K. J., D. Sbarbaro, R. Zbikowski, and P. J. Gawthrop. 1992. Neural networks for control systems-a survey. Automica 28:1083-1112.
16. Li, W., and J. J. E. Slotine. 1989. An indirect adaptive controller. Systems and Control Letters 12:259-266.
17. Miyamoto, H., M. Kawato, T. Setoyama, and R. Suzuki. 1988. Feedback error learning neural network for trajectory control of a robotic manipulator. Neural Networks 1:251-265.
18. Narendra, K. S., and K. Parthasarathy. 1990. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1:4-27.
19. Ozaki, T., T. Suzuki, T. Furuhashi, S. Okuma, and Y. Uchikawa. 1991. Trajectory control of robotic manipulators using neural networks. IEEE Transactions on Industrial Electronics 38:641-657.
20. Psaltis, D., A. Sideris, and A. Yamamura. 1988. A multilayered neural network controller. IEEE Control Systems Magazine 8:17-21.
21. Pham, D. T., and S. J. Oh. 1994. Adaptive control of a robot using neural networks. Robotica 12:553-561.
22. Sadegh, N. 1993. A perceptron network for functional identification and control of nonlinear systems. IEEE Transactions on Neural Networks 4:982-988.
23. Sadegh, N., and R. Horowitz. 1990. Stability and robustness analysis of a class of adaptive controllers for robotic manipulators. International Journal of Robotics Research 9:74-92.
24. Slotine, J. J. E., and W. Li. 1989. Composite adaptive control of robot manipulators. Automatica 25:509-519.
25. Schlueter, R. A., and A. H. Levis. 1973. The optimal linear regulator with state dependent sampling. IEEE Trans. Automat. Control AC-18:512-515.
26. Sundararajan, N., L. Chin, and Y. K. San. 1993. Selection of network and learning parameters for an adaptive neural robotic control scheme. Mecha-tronics 3:747-766.
27. Teshnehlab, M., and K. Watanabe. 1994. Self-tuning of computed torque gains by using neural networks with flexible structures. IEE Proceedings-Control Theory and Applications 141:235-242.
28. Widrow, B., and M. A. Lehr. 1990. 30 years of adaptive neural networks: per-ceptron, madaline and back propagation. Proceedings of the IEEE 78:1415-1442