Implementation of indirect neuro-control for a nonlinear two-robot MIMO system

Control Engineering Practice

Volumn 9, Issue 1, 2001, Pages 89-95

  Jang, Jun Oh ^a  

^a Uiduk University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

IDENTIFICATION (CONTROL SYSTEMS); INTELLIGENT ROBOTS; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; ROBOT LEARNING; ROBOTICS; VELOCITY CONTROL;

INDIRECT NEURO CONTROLLER; MULTI INPUT MULTI OUTPUT SYSTEMS;

INTELLIGENT CONTROL;

EID: 0035154533     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0967-0661(00)00100-3     Document Type: Article

References (19)

1. Canudas, C., Noel, P., Aubin, A., & Brogliato, B. (1991). Adaptive friction compensation in robot manipulators: Low velocities. International Journal of Robot Research, 10(3), 189-199.
2. Chuang, H. Y., & Liu, C. H. (1996). Techniques in cross-coupled digital adaptive feed-rate control for multiaxes machine tools. Control and Dynamic Systems, 72, 265-301.
3. Cui, X., & Shin, K. G. (1993). Direct control and coordination using neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 23, 686-697.
4. Craig, J. J. (1986). Introduction to robotics: Mechanics and control. Reading, MA: Addison-Wesley.
5. Friedland, B., & Park, Y. (1992). On adaptive friction compensation. IEEE Transactions on Automatic Control, 37(10), 1069-1612.
6. Hassan, M. F., & Singh, M. G. (1980). Decentralized controller with online interaction trajectory improvement. IEE Proceedings - Control Theory and Applications, 127, 142-148.
7. Hornik, K., Stinchombe, M., & White, S. H. (1989). Multilayer feedforward networks are universal approximator. Neural networks, 2, 359-366.
8. Jang, J. O., & Jeon, G. J. (1999). Implementation of indirect neurocontrol for nonlinear dynamic systems. Mechatronics, 9(6), 675-686.
9. Jang, J. O., & Jeon, G. J. (2000). A parallel neuro-controller for DC motors containing nonlinear friction. Neurocomputing, 30, 233-248.
10. Karakasoglu, A., Sudharsanan, S. I., & Sundareshan, M. K. (1993). Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulator. IEEE Transactions on Neural networks, 4(6), 4-27.
11. Kawato, M., Uno, Y., Isobe, M., & Suzuki, R. (1988). A hierarchical model for voluntary movement and its application to robotics. IEEE Control Systems Magazine, 8(4), 8-15.
12. Lee, T. H., Koh, E. K., & Loh, M. K. (1996). Stable adaptive control of multivariable servomechanisms, with application to a passive line-of-sight stabilization system. IEEE Transactions on Industrial Electronics, 43(1), 98-105.
13. Lightbody, G., & Irwin, G. W. (1995). Direct neural model reference adaptive control. IEE Proceedings - Control Theory and Applications, 142, 31-43.
14. Liu, Y. H., Xu, Y., & Bergerman, M. (1999). Cooperation control of multiple manipulators with passive joints. IEEE Transactions on Robotic Automation, 15(2), 258-267.
15. Mao, C. J., & Lin, W. S. (1990). Decentralized control of interconnected systems with unmodelled nonlinearity and interaction. Automatica, 26, 263-268.
16. Narendra, K. S., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4-27.
17. Rumelhart, D. E., Hinton, G. E., & Williams, G. E. (1986). Learning internal representations by error propagation. In D. E. Rumelhart, & J. L. McClelland, Parallel Distributed Processing, vol. 1 (pp. 318-362). Cambridge, MA, USA: MIT Press.
18. Zhu, W. H., & Schutter, J. D. (1999). Control of two industrial manipulators rigidly holding an egg. IEEE Control Systems Magazine, 19, 24-30.
19. Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controller. Transactions on ASME, 64, 759-768.