Abstract
To imitate human flexibility in controlling different complex non-linear processes on the basis of process observation and/or trial and error, learning control has been developed. The main elements of such control loops are interpolating memories. The chapter deals after an introduction to learning control loops with such devices by putting forward different alternatives, discussing their behaviour in general and going into details of recent research work on mathematically inspired interpolating memories. The respective improvements are motivated and results of applications in the areas of biotechnology and automotive control are presented. In a conclusion some further application areas and realisation aspects are discussed and a critical assessment of status and usefulness of learning control is given.
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
J.S. Albus. Theoretical and Experimental Aspects of a Cerebellar Model. PhD thesis, University of Maryland, Maryland, 1972.
J.S. Albus. A new approach to manipulator control: The cerebellar model articulation controller. Transactions ASME, 97(3), 1975.
Andrew R. Barron. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, 39(3):930–945, May 1993.
Martin Brown and Christopher J. Harris. Neurofuzzy Adaptive Modelling and Control. Prentice Hall, ISBN 0–13–134453–6, 1994.
E. Ersü and J. Militzer. Software implementation of a neuron-like associative memory system for control applications. In 2nd IASTED Conference on Mini- and Micro-Computer Applications — MIMI’82. Davos, Switzerland, March 1982.
Enis Ersü and Jürgen Militzer. Real-time implementation of an associative memory-based learning control scheme for non-linear multivariable processes. In IEE-Symposium: Application of Multivariable System Techniques, Plymouth, UK, 31. Oktober – 2. November 1984.
P. Funk. Variationsrechnung und ihre Anwendung in Physik und Technik. Springer Verlag, 2 edition, 1970.
Stefan Gehlen. Untersuchungen zur wissensbasierten und lernenden Prozeßführung in der Biotechnologie. PhD thesis, TH Darmstadt, FG Regelsystemtheorie & Robotik, 1993. Fortschritt-Berichte VDI, Reihe 20, Rechnerunterstützte Verfahren, Nr. 87, VDI-Verlag, ISBN 3–18–148720–1.
C. J. Harris, C. G. Moore, and M. Brown. Intelligent Control — Aspects of fuzzy logic and neural nets. World scientific, 1993.
Rolf Isermann. Digital Control Systems. Springer, 1981.
A. G. Ivankhenko. Heuristic self-organization in problems of engin. cybernetics. Automatica, 6, 1970.
K. Kleinmann, M. Hormel, and W. Paetsch. Intelligent real-time control of a multifingered robot gripper by learning incremental actions. In IFAC/IFIP/IMACS Int. Symp. on Artificial Intelligence in Real-Time Control. Delft, The Netherlands, June 1992.
K. Kleinmann and R. Wacker. On a selftuning decoupling controller for the joint control of a tendon driven multifingered robot gripper. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’94). Munich, 1994.
M. Kortmann and H. Unbehauen. Ein neuer Algorithmus zur automatischen Selektion der optimalen Modellstruktur bei der Identifikation nichtlinearer Systeme. Automatisierungstechnik (at), 1987.
A. Kurz. Building maps based on a learned classification of ultrasonic range data. In D. Charnley, editor, 1st IFAC Workshop on Intelligent Autonomous Vehicles. Pergamon Press, Southampton, Southampton, UK, April 1993.
R. P. Lippmann. An introduction to computing with neural nets. IEEE ASSP Magazine, April 1987.
Jürgen Militzer and Henning Tolle. Vertiefungen zu einem Teilbereiche der menschlichen Intelligenz imitierenden Regelungsansatz. In Jahrestagung der Deutschen Gesellschaft für Luft- und Raumfahrt, München, 1986.
W. Thomas Miller III, Filson H. Glanz, and L. Gordon Kraft III. Application of a general learning algorithm to the control of robotic manipulators. The International Journal of Robotics and Control, 6(2):84–98, 1987.
W. S. Mischo, M. Hormel, and H. Tolle. Neurally inspired associative memories for learning control. A comparison. In ICANN — 91, International Conference on Artificial Neural Networks. Espoo, Finland, June 1991.
Walter Sebastian Mischo and Henning Tolle. Ein assoziativer VLSI-Prozessor zur schnellen Informations-/Stellsignalgenerierung. In Fachtagung Integrierte mechanisch-elektronische Systeme, number 179 in VDI Fortschrittsberichte, pages 263–278. VDI Verlag, 1993.
W. Paetsch and M. Kaneko. A three fingered, multijoined gripper for experimental use. In IROS’90, Int. Workshop on Intelligent Robots and Systems. Tsuchiusa, Ibaraki, Japan, July 1990.
Jürgen Roth, Bert Breuer, and Jörg Stöcker. Kraftschlußerkennung im rotierenden Reifen. In Fachtagung Integrierte mechanisch-elektronische Systeme, number 179 in VDI Fortschrittsberichte, pages 132–143. VDI Verlag, 1993.
G. N. Saridis. Self-Organizing Control of Stochastic Systems. M. Dekker, 1977.
M. Schmitt and H. Tolle. Das Assoziativkennfeld, eine lernfähige Standardkomponente für Kfz-Steuergeräte. ATZ (Automobiltechnische Zeitschrift), 94(1), 1994.
Manfred Schmitt. Untersuchungen zur Realisierung mehrdimensionaler, lernfähiger Kennfelder in Großserien-Steuergeräten. PhD thesis, TH Darmstadt, 1994. — in preparation -.
H. Tolle, P. C. Parks, E. Ersü, M. Hormel, and J. Militzer. Learning control with interpolating memories — general ideas, design-lay-out, theoretical approaches and practical applications. Int. J. Control, 56, 1992.
Henning Tolle and Enis Ersü. Neurocontrol Number 172 in Lecture Notes in Control and Information Sciences. Springer-Verlag, 1992. ISBN 3–540–55057–7.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer-Verlag London Limited
About this chapter
Cite this chapter
Tolle, H., Gehlen, S., Schmitt, M. (1995). On Interpolating Memories for Learning Control. In: Hunt, K.J., Irwin, G.R., Warwick, K. (eds) Neural Network Engineering in Dynamic Control Systems. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-3066-6_7
Download citation
DOI: https://doi.org/10.1007/978-1-4471-3066-6_7
Publisher Name: Springer, London
Print ISBN: 978-1-4471-3068-0
Online ISBN: 978-1-4471-3066-6
eBook Packages: Springer Book Archive