Abstract
Multilayer perceptrons trained with the generalized delta rule or backpropagation procedure have been extensively applied in areas such as phoneme and image recognition and synthesis. Recently, many authors have reported interesting results on the application of these structures to the modeling and control of dynamical systems. In this article, we analyze the use of neural nets in a Model Reference adaptive Controller (MRAC) scheme for hydroturbine control. Several simulation results are provided, showing the feasibility of this approach and the factors involved in its design.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
J. S. Albus, “A new approach to manipulator control: The Cerebellar Model Articulation Controller,”Trans. ASME J. Dynam. Syst., Meas. Control vol. 97, pp. 220–227, September 1975.
W. T. Miller, “Sensor-based control of robotic manipulators using a general learning algorithm,”IEEE J. Robot. Automat. vol. RA-3, no. 2, pp. 157–165, April 1987.
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 Trans. Robot. Automat. vol. 6, no. 1, pp. 1–8, February 1990.
L. G. Kraft and D. P. Campagna, “A comparison between CMAC neural network control and two traditional adaptive control systems,”IEEE Control Syst. Mag. vol. 10, no. 3, pp. 36–43, April 1990.
R. P. Lippmann, “An introduction to computing with neural nets,”IEEE ASSP Mag. vol. 4, no. 2, pp. 4–22, April 1987.
J. L. McLelland, D. E. Rumelhart, and the PDP Research Group,Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, MIT Press: Cambridge, MA, 1988.
J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,”Proc. Natl. Acad. Sci. USA vol. 79, pp. 2554–2558, April 1982.
J. J. Hopfield and D. W. Tank, “Computing with neural circuits: A model,”Science vol. 233, pp. 625–633, August 1986.
D. Psaltis, A. Sideris, and A. A. Yamamura, “A multilayered neural network controller,”IEEE Control Syst. Mag. vol. 9, no. 3, pp. 17–21, April 1988.
S. Y. Kung and J. N. Hwang, “Neural network architectures for robotics applications,”IEEE Trans. Robot. Automat. vol. 5, no. 5, pp. 641–657, October 1989.
D. H. Nguyen and B. Widrow, “Neural networks for self-learning control systems,”IEEE Control Syst. Mag. vol. 10, no. 3, pp. 18–23, April 1990.
F. C. Chen, “Back propagation networks for nonlinear self tuning adaptive control,”IEEE Control Syst. Mag. vol. 10, no. 3, pp. 44–48, April 1990.
K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,”IEEE Trans. Neural Networks vol. 1, no. 1, pp. 4–27, March 1990.
R. H. Milocco and M. Sternad, “Optimal feed forward power-frequency control for hydro power station in interconnected systems,” UTEC 87 25 R, Institute of Technology, Uppsala University, February 1987.
R. J. McEliece, E. C. Posner, E. R. Rodemich, and S. S. Venkatesh, “The capacity of the Hopfield associative memory,”IEEE Trans. Inform. Theory vol. IT-33, no. 4, pp. 461–482, July 1987.
G. G. Mirchandani and W. Cao, “On hidden nodes for neural nets,”IEEE Trans. Circuits Syst., vol. 36, no. 5, May 1989.
E. B. Baum and D. Haussler, “What size net gives valid generalization?”Neural Comput. vol. 1, pp. 151–160, 1989.
S. C. Huang and Y. F. Huang, “Learning algorithms for perceptrons using back-propagation with selective updates,”IEEE Control Systems Mag. vol. 10, no. 3, pp. 56–61, April 1990.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Mayosky, M.A., Catalfo, J.M. & Acosta, G.G. Neural-net-based control of dynamical systems: A case study. Appl Intell 3, 267–274 (1993). https://doi.org/10.1007/BF00872132
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF00872132