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Modeling of a Coupled Industrial Tank System with ANFIS

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2972))

Abstract

Since liquid tank systems are commonly used in industrial applications, system-related requirements results in many modeling and control problems because of their interactive use with other process control elements. Modeling stage is one of the most noteworthy parts in the design of a control system. Although nonlinear tank problems have been widely addressed in classical system dynamics, when designing intelligent control systems, the corresponding model for simulation should reflect the whole characteristics of the real system to be controlled. In this study, a coupled, interacting, nonlinear liquid leveling tank system is modeled using ANFIS (Adaptive-Network-Based Fuzzy Inference System), which will be further used to design and apply a fuzzy-PID control to this system. Firstly, mathematical modeling of the system is established and then, data gathered from this model is employed to create an ANFIS model of the system. Both mathematical and ANFIS model is compared, model consistencies are discussed, and flexibility of ANFIS modeling is shown.

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References

  1. Patterson, D.W.: Artificial Neural Networks – Theory and Applications. Prentice-Hall, Englewood Cliffs (1996)

    MATH  Google Scholar 

  2. Engin, S.N., Gulez, K.: A Wavelet Transform – Artificial Neural Networks (WTANN) based Rotating Machinery Fault Diagnostics Methodology. In: IEEE NSIP 1999, Falez Hotel, Antalya, Turkey, June 1-3 (1999)

    Google Scholar 

  3. Staszewski, W.J., Worden, K.: Classification of Faults in Gearboxes - Pre-processing Algorithms and Neural Networks. Neural Computing and Applications 5(3), 160–183 (1997)

    Article  Google Scholar 

  4. Mamdani, E.H., Assilian, S.: An Experiment in Linguistic Synthesis with a Logic Controller. Int. J. Man – Machine Studies 8, 1–13 (1975)

    Article  Google Scholar 

  5. Takagi, S., Sugeno, M.: Fuzzy identification of fuzzy systems and it’s application to modelling and control. IEEE Trans. Systems Man Cybern. 15, 116–132 (1985)

    MATH  Google Scholar 

  6. Culliere, T., Titli, A., Corrieu, J.: Neuro-fuzzy modelling of nonlinear systems for control purposes, In Proc. IEEE INT. In: Conf. on Fuzzy Systems, Yokohama, pp. 2009–2016 (1995)

    Google Scholar 

  7. Nauck, D.: Fuzzy neuro systems: An overview. In: Kruse, R., Gebhardt, J., Palm, R. (eds.) Fuzzy Systems in Computer Science, pp. 91–107. Vieweg, Braunschweig (1994)

    Google Scholar 

  8. Jang, J.: ANFIS: Adaptive-Network Based Fuzzy Inference System. IEEE Trans. On Systems, Man and Cybernetics 23(3), 665–685 (1993)

    Article  MathSciNet  Google Scholar 

  9. Jang, J., Sun, C.T.: Neuro-Fuzzy Modeling and Control. IEEE Proc. 83(3), 378–406 (1995)

    Article  Google Scholar 

  10. Jang, J.: Input Selection for ANFIS Learning. IEEE Fuzzy Systems, 1493 – 1499 (1996)

    Google Scholar 

  11. Jang, J.: Neuro – Fuzzy Modeling for Dynamic System Identification. In: IEEE Fuzzy Systems Symposium, pp. 320–325 (1996)

    Google Scholar 

  12. Altug, S., Chow, M.: Fuzzy Inference Systems Implemented on Neural Architectures for Motor Fault Detection and Diagnosis. IEEE Trans. on Ind. Electronics 46(6) (December 1999)

    Google Scholar 

  13. Zhou, C., Jagannathan, K.: Adaptive Network Based Fuzzy Control of a Dynamic Biped Walking Robot. In: IEEE 1996 Int. Joint Symposia on Intelligence and Systems (IJSIS 1996), November 4-5 (1996)

    Google Scholar 

  14. Djukanović, M.B., Ćalović, M.S., Veśović, B.V., Šobajć, D.J.: Neuro – Fuzzy Controller of Low Head Hydropower Plants Using Adaptive – Network Based Fuzzy Inference System. IEEE Trans. on Energy Conversion 12(4) (December 1997)

    Google Scholar 

  15. Niestroy, M.: The use of ANFIS for Approximating an optimal controller. In: World Cong. On Neural Networks, San Diego, CA, September 15-18, pp. 1139–1142 (1996)

    Google Scholar 

  16. Jensen, E.W., Nebot, A.: Comparision of FIR and ANFIS Methodologies for Prediction of Mean Blood Pressure and Auditory Evoked Potentials Index During Anaesthesia. In: Proceedings of the IEEE Engineering in Medicine and Biology Society, vol. 20(3) (1998)

    Google Scholar 

  17. Oonsivilai, A., El – Hawary, M.E.: Power System Dynamic Modeling using Adaptive – Network Based Fuzzy Inference System. In: Proceedings of the 1999 IEEE Canadian Conf. on Electrical and Computer Engineering, Canada, May 9-12 (1999)

    Google Scholar 

  18. Lian, S.T., Marzuki, K., Rubiyah, M.: Tuning of a neuro-fuzzy controller by genetic algorithms with an application to a coupled-tank liquid-level control system. Engineering Application of Artificial Intelligence, 517–529 (1998)

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Engin, S.N., Kuvulmaz, J., Ömürlü, V.E. (2004). Modeling of a Coupled Industrial Tank System with ANFIS. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_83

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  • DOI: https://doi.org/10.1007/978-3-540-24694-7_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21459-5

  • Online ISBN: 978-3-540-24694-7

  • eBook Packages: Springer Book Archive

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