Open Access
ITM Web Conf.
Volume 64, 2024
2nd International Conference on Applied Computing & Smart Cities (ICACS24)
Article Number 01021
Number of page(s) 13
Published online 05 July 2024
  1. W-T. Huang and J-P. LI, Research and design of intelligent temperature control system. Proceeding of 2010 Second International Workshop on Education Technology and Computer Science, 2010, pp. 538–541. [Google Scholar]
  2. T. Hagglund, K. Astrom, Automatic tuning of PID controllers, in W.S. Levine (Ed), the Control Handbook, CRC Press, Boca Raton, FL, 1996, pp. 817–826. [Google Scholar]
  3. Muresan, C. I., & De Keyser, R. (2022). Revisiting Ziegler–Nichols. A fractional order approach. ISA transactions, 129, 287–296. [CrossRef] [Google Scholar]
  4. Zhu, J., Yang, Q., Lu, J., Zheng, B., & Yan, C. (2015). An adaptive artificial neural network-based supply air temperature controller for air handling unit. Transactions of the Institute of Measurement and Control, 37(9), 1118–1126. [Google Scholar]
  5. S. Muhammed, Internal model control structure using adaptive inverse control strategy. ISA Transaction vol. 44, 2005, pp. 353–362. [CrossRef] [Google Scholar]
  6. Mikhalevich, S. S., Baydali, S. A., & Manenti, F. (2015). Development of a tunable method for PID controllers to achieve the desired phase margin. Journal of Process Control, 25, 28–34. [CrossRef] [Google Scholar]
  7. S. Saini and S. Rani, Temperature Control Using Intelligent Techniques. Proceeding of 2012 Second International Conference on Advanced Computing & Communication Technologies, 2012, pp. 138–145. [Google Scholar]
  8. Z. Dong, Y. Su and X. Yan, Temperature Control System of the Thermal Analyzer Based on Fuzzy PID Controller. Proceeding of 2009 Ninth International Conference on Hybrid Intelligent Systems, 2009, pp. 58–61. [Google Scholar]
  9. J. Wei, Research on the Temperature Control System Based on Fuzzy Self-tuning PID, 2010 International Conference On Computer Design And Applications (ICCDA 2010), pp. 13–15. [Google Scholar]
  10. M. Sbarciog, R. Keyser, S. Cristea and C. Prada, Nonlinear predictive control of processes with variable time delay. A temperature control case study. Proceeding of 17th IEEE International Conference on Control Applications Part of 2008 IEEE Multiconference on Systems and Control San Antonio, Texas, USA, September 3-5, 2008, pp. 1001–1006. [Google Scholar]
  11. C.L. Lorenzetti. “Direct Water Heater Power Control for Reduced Harmonics and Flicker Content with Optimized Half-cycle Power Control”. Int. Revista da Propriedade Industrial, Vol 11, issue 3, PP. 175—180, Seção I. Rio de Janeiro, Brazil, November 2006. [Google Scholar]
  12. C.J. Savant, S. Roden and L. Gordan, Electronic design: circuits and systems, Benjamin/Cummings Publishing Company Inc. 1991. [Google Scholar]
  13. Miguel Castilla, Control Circuits in Power Electronics, The institution of engineering and Technology. London, United Kingdom. 2016. [Google Scholar]
  14. Q. Zhong, Robust control of time-delay systems, Springer – Verlag London Limited 2006. [Google Scholar]
  15. Halici, U., Leblebicioglu, K., Özgen, C., & Tuncay, S. (2018). Recent advances in neural network applications in process control. Recent Advances in Artificial Neural Networks, 229–289. [Google Scholar]
  16. Carrasco, J., García, S., Rueda, M. M., Das, S., & Herrera, F. (2020). Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm and Evolutionary Computation, 54, 100665. [Google Scholar]
  17. Wang, C. C., Kuo, P. H., & Chen, G. Y. (2022). Machine learning prediction of turning precision using optimized xgboost model. Applied Sciences, 12(15), 7739. [Google Scholar]
  18. Yasuoka, Y., Shinomiya, Y., & Hoshino, Y. (2016, August). Evaluation of optimization methods for neural network. In 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS) (pp. 92–96). IEEE. [Google Scholar]

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