Microrobotics and Control Engineering

Fuzzy-control and Artificial Neural Networks

Amount
3 LE, 1 TU, 6 CP

Responsible
Prof. Dr.-Ing. S. Fatikow

Syllabus

  • Control problems in robotics and automation technology
  • Basic ideas of fuzzy logic and ANN
  • Principles of fuzzy logic
  • Fuzzy logic of rule-based systems
  • ANN models
  • ANN learning rules
  • Multilayer perceptron networks and backpropagation
  • Associative networks
  • Self-organizing feature maps
  • PID design principles
  • Design of fuzzy control systems
  • Fuzzy logic application examples
  • Design of ANN control systems
  • ANN application examples
  • Fuzzy + Neuro: principles and applications
Aims
Experts in different branches try to approach their application-specific control and information processing problems by using fuzzy logic and artificial neural networks (ANN). The experiences gathered up to now prove robotics and automation technology to be predestined fields of application of both these approaches. The major topics of the course are control problems in robotics and automation technology, principles of fuzzy logic and ANN and their practical appplications, comparison of conventional and advanced control methods, combination of fuzzy logic and ANN in control systems. The course gives a comprehensive treatment of these advanced approaches for interested students. Reading
Essential:
  • Lecture notes (can be obtained for € 10,- in our secretariate)
Recommended:
  • Bothe, H.-H.: Neuro-Fuzzy-Methoden, Springer, 1998
  • Braun, Feulner, Malaka: Praktikum Neuronale Netze, Springer, 1997
  • Kahlert, J.: Fuzzy Control für Ingenieure, Vieweg, Braunschweig Wiesbaden, 1995
  • Nauck, D., Klawonn, F. und Kruse, R.: Neuronale Netze und Fuzzy-Systeme, Vieweg, 1994
  • Zell, A.: Simulation Neuronaler Netze, Addison-Wesley / Oldenbourg Verlag, Bonn, 1996
Secondary Literature:
  • Altrock, M. O. R.: Fuzzy Logic, R. Oldenbourg Verlag, 1993
  • Bekey, A. and Goldberg, K.Y. (Eds.): Neural Networks in Robotics, Kluwer Academic, 1996
  • Berns, K. und Kolb, T.: Neuronale Netze für technische Anwendungen, Springer, 1994
  • Bothe, H.-H.: Fuzzy Logic, Springer, 1993
  • Bunke, H., Kandel, A. (eds.): Neuro-Fuzzy Pattern Recognition, World Scientific Publ., 2000
  • Kahlert, J. und Hubert, F.: Fuzzy-Logik und Fuzzy-Control, Vieweg, 1993
  • Kim, Y.H. and Lewis, F.L.: High-Level Feedback Control with Neural Networks, World Scientific, 1998
  • Kratzer, K.P.: Neuronale Netze, Carl Hanser, 1993
  • Lämmel, U. und Cleve, J.: Künstliche Intelligenz (neuronale Netze), Fachbuchverlag Leipzig, 2001
  • Lawrence, J.: Neuronale Netze, Systhema Verlag, München, 1992
  • Omidvar, O. and van der Smagt, P. (eds.): Neural Networks for Robotics, Academic Press, 1997
  • Patterson, D.W.: Künstliche neuronale Netze, Prentice Hall, 1996
  • Pham, D.T. and Liu, X.: Neural Networks for Identification, Prediction and Control, Springer, 1997
  • Rigoll, G.: Neuronale Netze, Expert Verlag, Renningen-Malmsheim, 1994
  • Ritter, H., Martinetz, Th. und Schulten, K.: Neuronale Netze, Addison-Wesley, 1991
  • Schulte, U.: Einführung in Fuzzy-Logik, Franzis-Verlag, München, 1993
  • Tizhoosh, H.R.: Fuzzy-Bildverarbeitung, Springer, 1998
  • von Altrock, C.: Fuzzy Logic: Technologie, Oldenbourg, 1993
  • White, D. and Sofge, D. (Eds.): Handbook of Intelligent Control, Van Nostrand Reinhold, New York, 1992
  • Zakharian, S. Ladewig-Riebler, P. und Thoer, St.: Neuronale Netze für Ingenieure, Vieweg, Wiesbaden, 1998
  • Zalzala, A. and Morris, A. (Eds.): Neural Networks for Robotic Control, Ellis Horwood, London, 1996
  • Zimmermann H.-J. (Hrsg.): Datenanalyse, VDI-Verlag, 1995
  • Zimmermann, H.-J. (Hrsg.): Neuro + Fuzzy: Technologien und Anwendungen, VDI-Verlag, 1995
  • Zimmermann, H.-J. und von Altrock, C. (Hrsg.): Fuzzy Logic: Anwendungen, Oldenbourg, 1994
Pre-requisites: - Co-requisites: - Assessment: oral examination and course attendance
Methods of Assessment for Ratings 0-100:
  • course attendance: 20%
  • oral examination: 80%
Specialisation: Embedded Systems and Microrobotics Computer Science Area Choice: Systems Engineering