THERMAL SCIENCE

International Scientific Journal

THE PERFORMANCE OF PIP-CASCADE CONTROLER IN HVAC SYSTEM

ABSTRACT
Primitive controllers used in the early version for HVAC systems, like the on-off (Bang-Bang) controller, are inefficient, inaccurate, unstable, and suffer from high-level mechanical wear. On the other hand, other controllers like PI and cascade controllers, overcome these disadvantages but when an offset response (inaccurate response) occurs, power consumption will increase. In order to acquire better performance in the central air-conditioning system, PIP-cascade control is investigated in this paper and compared to the traditional PI and PID, in simulation of experimental data. The output of the system is predicted through disturbances. Based on the mathematical model of air-conditioning space, the simulations in this paper have found that the PIP-cascade controller has the capability of self-adapting to system changes and results in faster response and better performance.
KEYWORDS
PAPER SUBMITTED: 2013-08-12
PAPER REVISED: 2013-10-13
PAPER ACCEPTED: 2013-11-26
PUBLISHED ONLINE: 2014-07-06
DOI REFERENCE: https://doi.org/10.2298/TSCI130812183L
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2014, VOLUME 18, ISSUE Supplement 1, PAGES [S213 - S220]
REFERENCES
  1. Jiangjiang Wang, Youyin Jing and Dawei An, Study of Neuron Adaptive PID Controller in a Singlezone HVAC System. Proceedings First International Conference on Innovative Computing, Information and Control, 30 Aug. - 1. Sep. 2006, Beijing, China, vol. 2, pp. 142-145
  2. Szymon Ogonowski, Modeling of the heating system in small building for control. Energy and Buildings, vol.42, 2010, 9, pp. 1510-1516.
  3. Jiangjiang Wang, Chunfa Zhang and Youyin Jing, Application of an Intelligent PID Control in Heating Ventilating and Air-conditioning System. Proceedings, World Congress on Intelligent Control and Automation, Chongqing, China, 2008, pp. 4371-4376
  4. Servet Soyguder, Mehmet Karakose and Hasan Alli, Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system. Expert Systems with Applications, vol. 36, 2009, 3, part 1, pp. 4566-4573.
  5. Riccardo Scattolini, Architectures for distributed and hierarchical Model Predictive Control - A review.Journal of Process Control, vol. 19, 2009, 5, pp. 723-731.
  6. Antonio Visioli, Advances in Industrial Control. Second edition. Springer-Verlag. London, 2006
  7. Donald R. Coughanowr, Process Systems Analysis and Control. Second edition, chp. 15, 1991, pp. 177, McGraw-Hill Inc. New York, USA
  8. Xiaosh Lu and Martti Viljanan, Controlling building indoor temperature and reducing heating cost through night heating electric stove. Energy and Building, vol.33, 2001, 8, pp. 865-873.
  9. Xiaosh Lu and MarttiViljanan, Modeling of heat and moisture transfer in buildings. Energy and Building, vol.34, 2002, 10, pp. 1045-1054.
  10. Wang Jiang-Jiang, Zhang Chun-Fa and Jing You-Yin, Research of Cascade Control with an Application to Central Air-Conditioning System.Proceedings of International Conference on Machine Learning and Cybernetics, Honk Kong, vol. 1, 2007, pp. 498-503

© 2019 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence