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Iterative Feedback Tuning in Linear and Fuzzy Control Systems

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Abstract

Aspects concerning the design of linear and fuzzy control systems based on the Iterative Feedback Tuning (IFT) approach are discussed. Two types of controller parametric conditions are derived to guarantee the robust stability of the control systems. The conditions are included in the steps of the IFT algorithms of linear control systems. Next an IFT-based design of a class of Takagi-Sugeno PI-fuzzy controllers (PI-FCs) is given. The design method maps the parameters of the linear PI controllers onto the parameters of the Takagi-Sugeno PI-FCs. The application of IFT in linear and fuzzy control systems is exemplified in a case study dealing with the angular position control of a DC servo system with backlash laboratory equipment. The performance enhancement ensured by IFT and fuzzy control is illustrated by real-time experimental results.

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Author information

Authors and Affiliations

  1. Department of Automation and Applied Informatics, “Politehnica” University of Timisoara, Bd. V. Parvan 2, 300223, Timisoara, Romania

    Radu-Emil Precup, Mircea-Bogdan Rădac, Stefan Preitl & Claudia-Adina Dragoş

  2. School of Information Technology and Engineering, University of Ottawa, 800 King Edward, Ottawa, Ontario, Canada, K1N 6N5

    Emil M. Petriu

Authors
  1. Radu-Emil Precup

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  2. Mircea-Bogdan Rădac

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  3. Stefan Preitl

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  4. Emil M. Petriu

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  5. Claudia-Adina Dragoş

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Editor information

Editors and Affiliations

  1. John von Neumann Fac. Informatics, Dept. Intelligent Engineering Systems, Budapest Tech., 1034, Budapest, Hungary

    Imre J. Rudas  & János Fodor  & 

  2. Systems Research Instiute, PAN Warszawa, Newelska 6, 01-447, Warszawa, Poland

    Janusz Kacprzyk

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© 2009 Springer-Verlag Berlin Heidelberg

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Precup, RE., Rădac, MB., Preitl, S., Petriu, E.M., Dragoş, CA. (2009). Iterative Feedback Tuning in Linear and Fuzzy Control Systems. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds) Towards Intelligent Engineering and Information Technology. Studies in Computational Intelligence, vol 243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03737-5_13

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