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Temperature Controller Using the Takagi-Sugeno-Kang Fuzzy Inference System for an Industrial Heat Treatment Furnace

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12254))

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Abstract

The industrial welding industry has a high energy consumption due to the heating processes carried out. The heat treatment furnaces used for reheating equipment made of steel require a good regulator to control the temperature at each stage of the process, thereby optimizing resources. Considering dynamic and variable temperature behavior inside the oven, this paper proposes the design of a temperature controller based on a Takagi-Sugeno-Kang (TSK) fuzzy inference system of zero order. Considering the reaction curve of the temperature process, the plant model has been identified with the Miller method and a subsequent optimization based on the descending gradient algorithm. Using the conventional plant model, a TSK fuzzy model optimized by the recursive least square’s algorithm is obtained. The TSK fuzzy controller is initialized from the conventional controller and is optimized by descending gradient and a cost function. Applying this controller to a real heat treatment system achieves an approximate minimization of 15 min with respect to the time spent with a conventional controller. Improving the process and integrated systems of quality management of the service provided.

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References

  1. Leme, R.D., Nunes, A.O., Message Costa, L.B., Silva, D.A.L.: Creating value with less impact: lean, green and eco-efficiency in a metalworking industry towards a cleaner production. J. Clean. Prod. (2018)

    Google Scholar 

  2. Mufarroha, F.A., Utaminingrum, F.: Hand gesture recognition using adaptive network based fuzzy inference system and K-nearest neighbor. Int. J. Technol. (2017). https://doi.org/10.14716/ijtech.v8i3.3146

  3. Tamilselvan, G.M., Aarthy, P.: Online tuning of fuzzy logic controller using Kalman algorithm for conical tank system. J. Appl. Res. Technol. (2017). https://doi.org/10.1016/j.jart.2017.05.004

    Article  Google Scholar 

  4. Rajesh Jesudoss Hynes, N., Kumar, R., Shenbaga Velu, P., Angela Jennifa Sujana, J.: Optimization of friction stud welding process parameters by integrated Grey-Fuzzy logic approach. J. Appl. Res. Technol. (2018). https://doi.org/10.22201/icat.16656423.2018.16.4.724

  5. Garcia, C.A., et al.: MPC under IEC-61499 using low-cost devices for oil pipeline system. In: Proceedings - IEEE 16th International Conference on Industrial Informatics, INDIN 2018, pp. 659–664 (2018). https://doi.org/10.1109/INDIN.2018.8472094

  6. Zimit, A.Y., Yap, H.J., Hamza, M.F., Siradjuddin, I., Hendrik, B., Herawan, T.: Modelling and experimental analysis two-wheeled self balance robot using PID controller. In: Gervasi, O., et al. (eds.) ICCSA 2018. LNCS, vol. 10961, pp. 683–698. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95165-2_48

    Chapter  Google Scholar 

  7. Ortiz, J.P., Minchala, L.I., Reinoso, M.J.: Nonlinear robust H-infinity PID controller for the multivariable system quadrotor. IEEE Lat. Am. Trans. (2016). https://doi.org/10.1109/TLA.2016.7459596

    Article  Google Scholar 

  8. Buele, J., et al.: Interactive system for monitoring and control of a flow station using LabVIEW. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 583–592. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_55

    Chapter  Google Scholar 

  9. García, C.A., et al.: Fuzzy control implementation in low cost CPPS devices. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 162–167 (2017). https://doi.org/10.1109/MFI.2017.8170423

  10. Buele, J., Varela-Aldás, J., Santamaría, M., Soria, A., Espinoza, J.: Comparison between fuzzy control and MPC algorithms implemented in low-cost embedded devices. In: Rocha, Á., Ferrás, C., Montenegro Marin, C.E., Medina García, V.H. (eds.) ICITS 2020. AISC, vol. 1137, pp. 429–438. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40690-5_42

    Chapter  Google Scholar 

  11. Wei, G., Alsaadi, F.E., Hayat, T., Alsaedi, A.: A linear assignment method for multiple criteria decision analysis with hesitant fuzzy sets based on fuzzy measure. Int. J. Fuzzy Syst. 19(3), 607–614 (2016). https://doi.org/10.1007/s40815-016-0177-x

    Article  MathSciNet  Google Scholar 

  12. Salem, M., Mora, A.M., Merelo, J.J., García-Sánchez, P.: Evolving a TORCS modular fuzzy driver using genetic algorithms. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 342–357. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_24

    Chapter  Google Scholar 

  13. Ansari, Z., Ghazizadeh, R., Shokhmzan, Z.: Gradient descent approach to secure localization for underwater wireless sensor networks. In: 2016 24th Iranian Conference on Electrical Engineering, ICEE 2016 (2016). https://doi.org/10.1109/IranianCEE.2016.7585498

  14. Duarte, J., Orozco, W.: Optimización de sintonización de controladores PID bajo el criterio IAE aplicados a procesos térmicos. Rev. Fac. Ing. 5, 35–45 (2015)

    Google Scholar 

  15. Campos, J., Jaramillo, S., Morales, L., Camacho, O., Chavez, D.: PD + i Fuzzy Controller optimized by PSO applied to a variable dead time process. In: 2018 IEEE 3rd Ecuador Technical Chapters Meeting, ETCM 2018 (2018). https://doi.org/10.1109/ETCM.2018.8580277

  16. Díaz-Álvarcz, A., Serradilla-García, F., Jiménez-Alonso, F., Talavera-Muñoz, E., Olaverri-Monreal, C.: Fuzzy controller inference via gradient descent to model the longitudinal behavior on real drivers. In: IEEE Intelligent Vehicles Symposium, Proceedings (2019). https://doi.org/10.1109/IVS.2019.8814180

  17. Sakalli, A., Beke, A., Kumbasar, T.: Gradient descent and extended kalman filter based self-tuning interval type-2 fuzzy PID controllers. In: 2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016 (2016)

    Google Scholar 

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Authors and Affiliations

  1. SISAu Research Group, Universidad Tecnológica Indoamérica, 180212, Ambato, Ecuador

    Jorge Buele

  2. Escuela Superior Politécnica de Chimborazo, Riobamba, 060155, Ecuador

    Paulina Ríos-Cando & Rodrigo Moreno-P.

  3. Universidad Técnica de Ambato, Ambato, 180103, Ecuador

    Jorge Buele, Geovanni Brito & Franklin W. Salazar

Authors
  1. Jorge Buele
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  2. Paulina Ríos-Cando
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  3. Geovanni Brito
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  4. Rodrigo Moreno-P.
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  5. Franklin W. Salazar
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Corresponding author

Correspondence to Jorge Buele .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Buele, J., Ríos-Cando, P., Brito, G., Moreno-P., R., Salazar, F.W. (2020). Temperature Controller Using the Takagi-Sugeno-Kang Fuzzy Inference System for an Industrial Heat Treatment Furnace. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12254. Springer, Cham. https://doi.org/10.1007/978-3-030-58817-5_27

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  • DOI: https://doi.org/10.1007/978-3-030-58817-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58816-8

  • Online ISBN: 978-3-030-58817-5

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