Application of Feedforward Neural Networks to Dynamical System Identification and Control

IEEE Transactions on Control Systems Technology

Volumn 1, Issue 1, 1993, Pages 37-49

  Kuschewski, John G ^a   Żak, Stanislaw H ^a   Hui, Stefen ^b  

^a PURDUE UNIVERSITY   (United States)

^b SAN DIEGO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONTROLLERS; DIGITAL CONTROL SYSTEMS; DYNAMICS; IDENTIFICATION (CONTROL SYSTEMS); NONLINEAR CONTROL SYSTEMS;

ARTIFICIAL NEURAL NETWORKS; CONTROL ALGORITHMS; DYNAMICAL SYSTEMS; SYSTEM IDENTIFICATION;

NEURAL NETWORKS;

EID: 0027556807     PISSN: 10636536     EISSN: 15580865     Source Type: Journal    
DOI: 10.1109/87.221350     Document Type: Article

References (20)

1. J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Pwc. Nat. Acad. Sci., Vol. 79, pp. 2554-2558, Apr. 1982.
2. J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two state neurons,” Proc. Nat. Acad. Sci., vol. 81, pp. 3088–3092, May 1984.
3. J. J. Hopfield and D. W. Tank, “Computing with neural circuits: A model,” Sci., Vol. 233, pp. 625-633, Aug. 1986.
4. L. O. Chua and L. Yang, “Cellular neural networks: Theory,” IEEE Trans. Circuits Syst., Vol. 35, pp. 1257-1272, Oct. 1988.
5. B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: Perceptron, madaline, and backpropagation,” Pwc. IEEE, Vol. 78, pp. 1415-1442, Sept. 1990.
6. D. Psaltis, A. Sideris, and A. A. Yamamura, “A multilayered neural network controller,” IEEE Contr. Syst. Mag., Vol. 8, pp. 17-21, Apr. 1988.
7. M. Kawato, Y. Uno, M. Isobe, and R. Suzuki, “Hierarchical neural network model for voluntary movement with application to robotics,” IEEE Contr. Syst. Mag., Vol. 8, pp. 8-16, Apr. 1988.
8. D. H. Nguyen and B. Widrow, “Neural networks for self-learning control systems,” IEEE Contr. Syst. Mag., Vol. 10, pp. 18-23, Apr. 1990.
9. K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, Vol. 1, pp. 4-27, Mar. 1990.
10. F. Chen, “Back-propagation neural networks for nonlinear self-tuning adaptive control,” IEEE Contr. Syst. Mag., Vol. 10, pp. 44-48, Apr. 1990.
11. Special issue on intelligent control, Int. J. Contr., Vol. 56, Aug. 1992.
12. S. H. Żak and H. J. Sira-Ramirez, “On the adaptation algorithms for feedforward neural networks,” in Theoretical Aspects of Industrial Design, D. A. Field and V. Komkov, Eds. New York: SIAM, 1990, pp. 116-133.
13. B. Widrow and R. Winter, “Neural nets for adaptive filtering and adaptive pattern recognition,” IEEE Comput. Mag., Vol. 21, pp. 25-39, Mar. 1988.
14. G. C. Goodwin and K. S. Sin, Adaptive Filtering Prediction and Control. Englewood Cliffs, NJ: Prentice-Hall, 1984.
15. H. J. Sira-Ramirez and S. H. Zak, “The adaptation of perceptrons with applications to inverse dynamics identification of unknown dynamic systems,” IEEE Trans. Syst., Man, Cybern., Vol. 21, pp. 634-643, May 1991.
16. S. H. Zak and C. A. MacCarley, “State feedback control of nonlinear systems,” Int. J. Contr., vol. 43, no. 5, pp. 1497-1514, 1986.
17. M. K. Sain and J. L. Peczkowski, “Nonlinear control by coordinated feedback synthesis with gas turbine applications,” Dep. Elec. Eng., Univ. Notre Dame, Notre Dame, IN, Contr. Syst. Tech. Rep. 36, Nov. 1985, pp. 236–243.
18. J. L. Peczkowski and M. K. Sain, “Design of nonlinear multivariable feedback controls by total synthesis,” Dep. Elec. Eng., Univ. Notre Dame, Notre Dame, IN, Contr. Syst. Tech. Rep. 36, Nov. 1985, pp. 206–215.
19. B. L. Walcott, M. J. Corless, and S. H. Zak, “Comparative study of nonlinear state-observation techniques,” Int. J. Contr., vol. 45, no. 6, pp. 2109-2132, 1987.
20. T. I. Pallett and S. Ahmad, “Real-time helicopter flight control: Modeling and control by linearization and neural networks,” School Elec. Eng., Purdue Univ., West Lafayette, IN, Tech. Rep.TR-EE 91-35, 1991.