Abstract
Fuzzy controllers are on their way of becoming a standard tool in industrial automation. [13] gives an overview of possible control concepts involving fuzzy components. It turns out that the application of fuzzy control is particularly effective at the higher levels of automation systems. For this purpose, direct fuzzy controllers are usually designed manually. Experts’ knowledge is used to determine the membership functions and the rule base (Fig. 1). This approach allows a fast controller prototyping, but the optimization of the controller usually requires a tedious tuning procedure due to the great number of free parameters and incomplete heuristic knowledge.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. Babuska, J. Sousa, and H. B. Verbmggen. Model-based design of fuzzy control systems. In Proceedings of the Third European Congress on Fuzzy and Intelligent Technologies, Aachen, Germany, pages 837–841, 1995.
R. Babuska and H. B. Verbruggen. An overview of fuzzy modeling for control. Control Engineering Practice, 4(11):1593–1606, 1996.
L. Boullart, A. Krijgsman, and R. A. Vingerhoeds, editors. Application of Artificial Intelligence in Process Control. Pergamon Press, Oxford, 1992.
H. A. B. te Braake, R. Babuska, and E. v. Can. Fuzzy and neural models in predictive control. Journal A, 35(3):44–51, 1994.
M. Brown and C. Harris. Neurofuzzy Adaptive Modelling and Control. Prentice Hall, New York, 1994.
J. C. Doyle, B. A. Francis, and A. R. Tannenbaum. Feedback Control Theory. Macmillan Publishing Co., New York, 1992.
C. G. Economou, M. Morari, and B. Palsson. Internal model control. 5. extension to nonlinear systems. Ind. Eng. Chem. Process Des. Dev., 21:403–411, 1986.
M. Fischer and R. Isermann. Robust hybrid control based on inverse fuzzy process models. In Proceedings of the IEEE International Conference on Fuzzy Systems, New Orleans, LA, USA, pages 1210–1216, 1996.
M. Fischer and O. Nelles. Fuzzy model-based predictive control of nonlinear processes with fast dynamics. In Proceedings of the Second International ICSC Symposium on Fuzzy Logic and Applications, Zürich, Switzerland, pages 57–63, 1997.
M. Fischer, O. Nelles, and D. Füssel. Tuning of pid-controllers for nonlinear processes based on local linear fuzzy models. In Proceedings of the Fourth European Congress on Fuzzy and Intelligent Technologies, Aachen, Germany, pages 1891–1895, 1996.
K. J. Hunt, R. Haas, and R. Murray-Smith. Extending the functional equivalence of radial basis function networks and fuzzy inference systems. IEEE Transactions on Neural Networks, 7(3):776–781, 1996.
K. J. Hunt, D. Sbarbaro, R. Zbikowski, and P.J. Gawthrop. Neural networks for control systems — a survey. Automatica, 28(6): 1083–1112, 1992.
R. Isermann. On fuzzy logic applications for automatic control, supervision and fault diagnosis. In Proceedings of the Third European Congress on Fuzzy and Intelligent Technologies, Aachen, Germany, pages 738–753, 1995.
R. Isermann, K.-H. Lachmann, and D. Matko. Adaptive Control Systems. Prentice Hall, New York, 1992.
T.A. Johansen. Fuzzy model based control: Stability, robustness, and performance issues. IEEE Transactions on Fuzzy Systems, 2(3):221–234, 1994.
M. I. Jordan and D. E. Rumelhart. Forward models: Supervised learning with a distal teacher. Cognitive Science, 16(3):307–354, 1992.
V. Kecman and B.-M. Pfeiffer. Exploiting the structural equivalence of learning fuzzy systems and radial basis function neural networks. In Proceedings of the Second European Congress on Fuzzy and Intelligent Technologies, Aachen, Germany, pages 58–66, 1994.
J. Moody and C. Darken. Fast learning in networks of locally-tuned processing units. Neural Computation, 1(2):281–294, 1989.
M. Morari and E. Zafiriou. Robust Process Control Prentice Hall, Englewood Cliffs, 1989.
R. Murray-Smith. A Local Model Network Approach to Nonlinear Modeling. PhD thesis, University of Strathclyde, UK, 1994.
O. Nelles and R. Isermann. Identification of nonlinear dynamic systems-classical methods versus radial basis function networks. In Proceedings of the American Control Conference, Seattle, WA, USA, pages 3786–3790, 1995.
O. Nelles and R. Isermann. Basis function networks for interpolation of local linear models. In Proceedings of the IEEE Conference on Decision and Control, Kobe, Japan, 1996.
K. Ohnishi. A new servo method in mechatronics. Transactions of Japanese Society of Electrical Engineers, 107(D):83–86, 1987.
D. Psaltis, A. Sideris, and A. A. Yamamura. A multilayered neural network controller. IEEE Control Systems Magazine, 8(2): 17–21, 1988.
M. Sugeno and G.T. Kang. Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(l):15–33, 1988.
T. Takagi and M. Sugeno. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1):116–132, 1985.
T. Umeno and Y. Hori. Two degrees of freedom controllers for robust servomechanism: Their application to robot manipulators without speed sensors. In Proceedings of the 1990 IEEE International Workshop on Advanced Motion Control, Yokohama, Japan, pages 179–188, 1990.
T. Umeno and Y. Hori. Robust speed control of dc servomotors using modern two degrees-of-freedom controller design. IEEE Transactions on Industrial Electronics, 38(5):363–368, 1991.
L.-X. Wang. Adaptive Fuzzy Systems and Control — Design and Stability Analysis. Prentice Hall, Englewoods Cliffs, 1994.
Y. W. Yoshinari, W. Pedrycz, and K. Hirota. Construction of fuzzy models through clustering techniques. Fuzzy Sets and Systems, 54(2): 157–165, 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Fischer, M., Isermann, R. (1998). Inverse Fuzzy Process Models for Robust Hybrid Control. In: Driankov, D., Palm, R. (eds) Advances in Fuzzy Control. Studies in Fuzziness and Soft Computing, vol 16. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1886-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1886-4_5
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-662-11053-9
Online ISBN: 978-3-7908-1886-4
eBook Packages: Springer Book Archive