Predictive multiple model switching control with the self-organizing map

International Journal of Robust and Nonlinear Control

Volumn 12, Issue 11, 2002, Pages 1029-1051

  Motter, Mark A ^a   Principe, Jose C ^b  

^a NASA LANGLEY RESEARCH CENTER   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Local dynamic modelling; Multiple model predictive control; Nonlinear system identification; Self organizing map

Indexed keywords

COMPUTER SIMULATION; CONTROL SYSTEM ANALYSIS; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; NATURAL FREQUENCIES; NONLINEAR CONTROL SYSTEMS; PREDICTIVE CONTROL SYSTEMS; SWITCHING; TOPOLOGY; TRANSONIC AERODYNAMICS; WIND TUNNELS;

LOCAL DYNAMIC MODELING; MULTIPLE MODEL PREDICTIVE CONTROL;

SELF ORGANIZING MAPS;

EID: 0036737055     PISSN: 10498923     EISSN: None     Source Type: Journal    
DOI: 10.1002/rnc.730     Document Type: Article

References (21)

1. Clarke DW, Mohtadi C, Tuffs PS. Generalized predictive control - Part I. The basic algorithm. Automatica 1987; 23(2):137-148.
2. Soeterboek RAM, et al. A predictive controller for the Mach number in a transonic wind tunnel. IEEE Control Systems Magazine 1991; 11(11).
3. Narendra KS, Balakrishnan J. Adaptive control using multiple models. IEEE Transactions on Automatic Control 1997; 42(2):171-187.
4. Kohonen T. The self-organizing map. Proceedings of the IEEE 1990; 78(9).
5. Kohonen T. Self-Organizing Maps. Springer-Verlag: Berlin,1995.
6. Principe J, Wang L. Nonlinear time series modelling with self-organizing maps. IEEE Workshop on Neural Networks for Signal Processing, 1995; 11-20.
7. Narendra KS, Mukhopadhyay S. Adaptive control using neural networks and approximate models. IEEE Transactions on Neural Networks 1997; 8(3):475-485.
8. Capone FJ, Bangert LS, Asbury SC, Mills CT, Bare EA. The NASA Langley 16-Foot Transonic Tunnel. NASA Technical Paper 3521, September 1995.
9. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error back-propagation. In Parallel Distributed Processing. The MIT Press: Cambridge, 1986.
10. Werbos, PJ. Back propagation through time: what it does and how to do it. Proceedings of the IEEE 1990; 78(10):1550-1560.
11. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilitites. Proceedings of the National Academy of Sciences of the U.S.A. 1982; 81:3088-3092.
12. Willshaw DJ, von der Malsburg C. How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London, Series B 1976; 194:431-445.
13. Haykin S. Neural Networks: A Comprehensive Foundation. Macmillan College: New York, 1994.
14. Robbins H, Monroe S. A stochastic approximation method. Annals of Mathematical Statistics 1951; 22:400-407.
15. Ritter H, Martinez M, Schulten K. Neural Computation and Self-Organizing Maps. Addision-Wesley: Reading, MA, 1992.
16. Ritter H. Self-organizing maps for robot control. Proceedings of the 7th International Conference on Artificial Neural Networks, 1997; 675-684.
17. Narendra KS. Neural networks for control: theory and practice. Proceedings of the IEEE 1996; 84(10).
18. Narendra KS, Balakrishnan J, Ciliz MK. Adaptation and learning using multiple models, switching, and tuning. IEEE Control Systems Magazine 1995; 15(3).
19. Narendra KS, Li S, Cabrera JBD, Intelligent control using neural networks. Proceedings of the 8th Yale Workshop on Adaptive and Learning Systems, 1994.
20. Narendra KS, Xiang C. Adaptive control of discrete-time systems using multiple models. IEEE Transactions on Automatic Control. 2000; 45(9):1669-1686.
21. Takens F. Detecting strange attractors in turbulence. Dynamical Systems and Turbulence. Springer-Verlag, New York, 1980; 365-381.