Skip to main content
SpringerLink
Log in

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

  • Published:
Journal of Control Theory and Applications Aims and scope Submit manuscript

Abstract

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Q. Bi, W. Cai, Q. Guo. Advanced controller auto-tuning and its application in HVAC systems[J]. Control Engineering Practice, 2000, 8(6): 633–644.

    Article  Google Scholar 

  2. J. Richalet, S. Abu El Ata-Doss, C. Arber, et al. Predictive functional control: application to fast and accurate robots[C]//Proceedings of 10th IFAC world Congress. Oxford: Pergamon, 1987: 251–258.

    Google Scholar 

  3. H. B. Kuntze et al. On the predictive functional control of an elastic industrial robots[C]//Proceedings of the 25th IEEE Conference on Decision and Control. New York: IEEE Press, 1986: 1877–1881.

    Google Scholar 

  4. Z. Zhan, S. Wang, G. Rong. MIMO bilinear systems predictive functional control based on exact linearization[J]. Control Theory & Application, 2003, 20(3): 477–480.

    Google Scholar 

  5. V. D. Moreira, W. C. Amaral, P. A. Ferreira. A new approach for robust model predictive control with an application to an air conditioning system[C]//The IEEE International Conference on Control Applications. Piscataway, New Jersey: IEEE Press, 2004: 667–672.

    Google Scholar 

  6. M. Lepetic, I. Skrjanc, H. G. Chiacchiarini, et al. Predictive functional control based on fuzzy model: magnetic suspension system case study[J]. Engineering Application of Artificial Intelligence, 2003, 16(3): 425–543.

    Article  Google Scholar 

  7. L. Lu, W. Cai, Y. Chaisoh, et al. HVAC systems optimization-condenser water loop[J]. Energy Conversion and Management, 2004, 45(4): 613–630.

    Article  Google Scholar 

  8. T. Takagi, M. Sugeno. Fuzzy identification of systems and its applications to modeling and control[J]. IEEE Transactions on Systems, Man and Cybernetics, 1985, 15(1): 116–132.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, Shandong, 250061, China

    Hong Lü & Lei Jia

  2. School of Mathematics Science, Qufu Normal University, Qufu, Shandong, 273165, China

    Shulan Kong & Zhaosheng Zhang

Authors
  1. Hong Lü
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shulan Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhaosheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

This work was supported by Young Scientists Fundamental Research Program of Shandong Province of China (No. 031B5147).

Hongli LÜ received her B.S. in mathematics in 2000, and M.S. in control theories in 2003 from Qufu Normal University, respectively. Since August, 2003 she has been a Ph.D. candidate of control science and engineering school, Shandong University. Since March, 2006 she has been a visiting scholar of EEE School, Nanyang Technological University. Her search interests include fuzzy control systems theories and application, model predictive control, nonlinear multivariable control systems, etc.

Lei JIA is currently the Head of the School of Control Science and Technology, Shandong University, China. He received his B.Eng. degree in Automation and the M.Eng degree in Control Engineering from Shandong University of Technology, China, in 1982 and 1988, respectively, and the Ph.D. degree in Control Theory and Control Engineering from Zhejiang University, China, in 1991. Sine 1995, he has been a Professor in Shandong University. His current research interests include Artificial intelligent and intelligent control, robust control, nonlinear process control theory and applications.

Shulan KONG is an associate professor at School of Mathematics-Sciences, Qufu Normal University. She received her M.S. and Ph.D. degrees in mathematics from Qufu Normal University and Shandong University in 1995 and 2004, respectively. Her research interests include nonlinear control systems, wireless communication systems and combinatorial optimization.

Zhaosheng ZHANG is an associate professor at School of Mathematics Sciences, Qufu Normal University. he received her M.S. and Ph.D. degrees in mathematics from Qufu Normal University and Shandong University in 1994 and 2004, respectively. His research interests include nonlinear control systems, intelligent control systems and combinatorial optimization.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lü, H., Jia, L., Kong, S. et al. Predictive functional control based on fuzzy T-S model for HVAC systems temperature control. J. Control Theory Appl. 5, 94–98 (2007). https://doi.org/10.1007/s11768-005-5301-7

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11768-005-5301-7

Keywords

Search

Navigation