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A Comparison of Tuning Methods for PID-Controllers with Fuzzy and Neural Network Controllers

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Cyber-Physical Systems and Control (CPS&C 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 95))

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Abstract

Conventional approaches to control systems still present a reasonable solution for a variety of different tasks in control engineering problems. Controllers based on the PID approach are used in a wide range of applications due to their easy handling, realization and set up, as well as their modest need of computational resources during the runtime. In order to heuristically find near-optimal parameters for the controller design, different approaches to tuning PID controllers have been developed. The Ziegler–Nichols methods are still commonly used despite that they have long been known, though modern methods, such as the T-Sum method, have also emerged. In this work, a comparison of the tuned PID controllers with a Mamdani-Fuzzy-Logic controller and an adaptive neural network controller is offered. A unified step response is used to classify the performance of controllers. It is shown that a PID control can work just as well as a fuzzy logic or neural network control for simple applications with time-invariant parameters or in applications where the parameters only change slightly and no strict constancy of the plant output is necessary.

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References

  1. Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. Trans. ASME 64, 759–768 (1942)

    Google Scholar 

  2. Kuhn, U.: Eine praxisnahe Einstellregel für PID-Regler: Die T-Summen-Regel. Automati-sierungstechnische Praxis 5, 10–16 (1995)

    Google Scholar 

  3. Kumar, R., Singla, S., et al.: Comparison among some well known control schemes with different tuning methods. J. Appl. Res. Technol. 13, 409–415 (2015)

    Article  Google Scholar 

  4. Wang, L., Freeman, C., Roger, E.: Experimental evaluation of automatic tuning of PID controllers for an electro-mechanical system. IFAC PapersOnLine 50(1), 3063–3068 (2017)

    Article  Google Scholar 

  5. Sung, S.W., Lee, I.-B.: On-line process identification and PID controller autotuning. Korean J. Chem. Eng. 16(1), 45–55 (1999)

    Article  Google Scholar 

  6. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  7. Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Electr. Eng. 121(12), 1585–1588 (1974)

    Article  Google Scholar 

  8. Lugli, A.B., et al.: Industrial application control with fuzzy systems. Int. J. Innov. Comput. Inf. Control 12(2), 665–676 (2016)

    Google Scholar 

  9. McCulloch, W., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5(4), 115–133 (1943)

    Article  MathSciNet  Google Scholar 

  10. Agarwal, M.: A systematic classification of neural-network-based control. IEEE Control Syst. Mag. 17(2), 75–93 (1997)

    Article  Google Scholar 

  11. Deng, J., et al.: Self-tuning PID-type fuzzy adaptive control for CRAC in datacenters. In: Springer IFIP Advances in Information and Communication Technology (AICT), vol. 419, no. 1, pp. 215–225 (2014)

    Chapter  Google Scholar 

  12. Shuzhi, S.G., Hang, C., Woon, L.: Adaptive neural network control of robot manipulators in task space. IEEE Trans. Industr. Electron. 44(6), 746–752 (1997)

    Article  Google Scholar 

  13. Potekhin, V.V., Pantyukhov, D.N., Mikheev, D.V.: Intelligent control algorithms in power industry. EAI Endorsed Trans. Energy Web 3(11), e5 (2017)

    Article  Google Scholar 

  14. Lunze, J.: Regelungstechnik 1 – System-theoretische Grundlagen, Analyse und Entwurf einschleifiger Regelungen. Springer, Heidelberg (2010)

    Google Scholar 

  15. Chien, K.L., Hrones, J.A., Reswick, J.B.: On the automatic control of generalized passive systems. Trans. ASME 74, 175–185 (1952)

    Google Scholar 

  16. Åström, K.J., Hägglund, T.: Automatic tuning of simple regulators with specifications on phase and amplitude margins. Automatica 20(5), 645–651 (1984)

    Article  MathSciNet  Google Scholar 

  17. Passino, K.M., Yurkovich, S.: Fuzzy Control. Addison Wesley Longman Inc., Menlo Park (1998)

    MATH  Google Scholar 

  18. Sugeno, M.: Industrial Applications of Fuzzy Control. Elsevier Science Inc., New York (1985)

    MATH  Google Scholar 

  19. Haykin, S.: Neural Networks - A Comprehensive Foundation. Pearson Education (Singapore) Pte. Ltd., Delhi (2005)

    Google Scholar 

  20. Patterson, J., Gibson, A.: Deep Learning - A Practicioner’s Approach. O’Reilly Media Inc, Sebastopol (2017)

    Google Scholar 

  21. Liu, G.: Nonlinear Identification and Control - A Neural Network Approach. Springer, London (2001)

    Book  Google Scholar 

  22. Rovithakis, G.A., Christodoulou, M.A.: Adaptive Control with Recurrent High-Order Neural Networks. Springer, London (2000)

    Book  Google Scholar 

  23. Halfmann, C., Holzmann, H.: Adaptive Modelle für die Kraftfahrzeugdynamik. Springer, Heidelberg (2003)

    Book  Google Scholar 

  24. Soloway, D., Haley, P.: Neural generalized predictive control. In: Proceedings of the 1996 IEEE International Symposium on Intelligent Control, Dearborn, MI, USA (1996)

    Google Scholar 

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Clemens Gross & Hendrik Voelker

Authors
  1. Clemens Gross
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  2. Hendrik Voelker
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Corresponding author

Correspondence to Clemens Gross .

Editor information

Editors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Dmitry G. Arseniev

  2. Institute of Transport and Automation Technology, Leibniz Universität Hannover, Garbsen, Niedersachsen, Germany

    Ludger Overmeyer

  3. Machine Vision and Pattern Recognition Laboratory, Lappeenranta University of Technology, Lappeenranta, Finland

    Heikki Kälviäinen

  4. Institute of Production Engineering and Photonic Technologies, Vienna University of Technology, Vienna, Austria

    Branko Katalinić

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Gross, C., Voelker, H. (2020). A Comparison of Tuning Methods for PID-Controllers with Fuzzy and Neural Network Controllers. In: Arseniev, D., Overmeyer, L., Kälviäinen, H., Katalinić, B. (eds) Cyber-Physical Systems and Control. CPS&C 2019. Lecture Notes in Networks and Systems, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-030-34983-7_8

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  • DOI: https://doi.org/10.1007/978-3-030-34983-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34982-0

  • Online ISBN: 978-3-030-34983-7

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