Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Optimal PID Control for Temperature Control of Chiller Equipment

칠러장비의 온도제어를 위한 최적 PID 제어

  • Park, Young-shin (Department of Industrial & Systems Engineering, Kongju National University) ;
  • Lee, Dongju (Department of Industrial & Systems Engineering, Kongju National University)
  • 박영신 (공주대학교 산업시스템공학과) ;
  • 이동주 (공주대학교 산업시스템공학과)
  • Received : 2022.08.29
  • Accepted : 2022.09.14
  • Published : 2022.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The demand for chiller equipment that keeps each machine at a constant temperature to maintain the best possible condition is growing rapidly. PID (Proportional Integral Derivation) control is a popular temperature control method. The error, which is the difference between the desired target value and the current system output value, is calculated and used as an input to the system using a proportional, integrator, and differentiator. Through such a closed-loop configuration, a desired final output value of the system can be reached using only the target value and the feedback signal. Furthermore, various temperature control methods have been devised as the control performance of various high-performance equipment becomes important. Therefore, it is necessary to design for accurate data-driven temperature control for chiller equipment. In this research, support vector regression is applied to the classic PID control for accurate temperature control. Simulated annealing is applied to find optimal PID parameters. The results of the proposed control method show fast and effective control performance for chiller equipment.

Keywords

References

  1. Callender, A., Hartree, D.R., and Porter, A., Time lag in a control system, Philos. Trans. R. Soc. London Series A, London, UK, Cambridge University Press, 1936.
  2. Cortes, C. and Vapnik, V., Support-vector networks, Machine Learning, 1995, Vol. 20, pp. 273-297.
  3. Dai, A., Zhou, X., and Wu, Z., Design of an intelligent controller for a grain dryer: A support vector machines for regression inverse model proportional-integral-derivative controller, Food Science & Nutrition, 2020, Vol. 8, pp. 805-819. https://doi.org/10.1002/fsn3.1340
  4. Drucker, H., Burges, C.J., Kaufman, L., Smola, A., and Vapnik, V.N., Support vector regression machines. In: Mozer M.C., Jordan M.I., and Petsche T. (Eds.), Advances in Neural Information Processing Systems 9, MIT Press, Cambridge, MA, 1997, pp. 155-161
  5. Holland, J.H., Genetic algorithms, Scientific American, 1992, Vol. 267, No. 1, pp. 66-73. https://doi.org/10.1038/scientificamerican0792-66
  6. Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P., Optimization by Simulated Annealing, Science, 1983, Vol. 220, pp. 671-680. https://doi.org/10.1126/science.220.4598.671
  7. Kumar, R., Srivastava, S., and Gupta, J.R.P., Artificial Neural Network based PID controller for online control of dynamical systems, IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2016, pp. 1-6.
  8. Li, Z., Support vector machine model based predictive pid control system for cement rotary kiln, Chinese Control and Decision Conference (CCDC), Xuzhou, Publisher: IEEE, 2010, pp. 3117-3121.
  9. Ucak, K. and Oke, G., Adaptive PID controller based on online LSSVR with kernel tuning, International Symposium on Innovations in Intelligent Systems and Applications, 2011, pp. 241-247.
  10. Xu, X., Wang, S., and Huang, G., Robust MPC for temperature control of air-conditioning systems concerning on constraints and multitype uncertainties, Building Services Engineering Research and Technology, 2010, Vol. 31, No.1, pp. 39-55. https://doi.org/10.1177/0143624409352420
  11. Ziegler, J.G. and Nichols, N.B., Optimum Settings for Automatic Controllers, Journal of Dynamic Systems, Measurement and Control, 1993, Vol. 115, pp. 220-222. https://doi.org/10.1115/1.2899060
  12. Zhao, J., Sun, W., Song, Y., and Wang, X., Fault-Tolerant PID controllers Design for Unknown Nonlinear Systems Based on Support Vector Machine, Proceedings of the Control and Decision Conference (CCDC), 2010, 2010 Chinese.