Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26673
Title: Development of an adaptive Smith predictor-based self-tuning PI controller for an HVAC system in a test room
Authors: Bai, J
Wang, S 
Zhang, X
Keywords: Adaptive control
Air-conditioning
PI controller
Smith predictor
Issue Date: 2008
Publisher: Elsevier
Source: Energy and buildings, 2008, v. 40, no. 12, p. 2244-2252 How to cite?
Journal: Energy and buildings 
Abstract: This paper presents an adaptive Smith predictor-based self-tuning PI controller and its application to the air-conditioning system of a test room. The significant time delay of air-conditioning processes can lead to degradation of performance and instability of the control loop. The parameters of air-conditioning processes vary due to the changes in the operation conditions. By using a recursive least squares (RLS) algorithm combined with a z-domain fitting method, the parameters of the air-conditioning process in the closed loop including time delay can be estimated online. Based on the estimated dead-time, a Smith predictor, which uses a reference model, is adopted to reduce the unfavorable effects of the time delay in the air-conditioning system. Based on the predicted error and estimated values, the control signal of the control loop is calculated by a self-tuning PI controller using ITAE tuning rules. The performance, robustness and effectiveness of the proposed control method are validated in the experimental platform. The corresponding performance of the proposed control method is compared with an adaptive PI controller. Experiment results demonstrate that the proposed strategy achieves better performance compared with the adaptive PI controller considering the effects of set-point changes, parameter variations or load disturbances in HVAC systems.
URI: http://hdl.handle.net/10397/26673
ISSN: 0378-7788
EISSN: 1872-6178
DOI: 10.1016/j.enbuild.2008.07.002
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

40
Last Week
0
Last month
0
Citations as of Sep 22, 2017

WEB OF SCIENCETM
Citations

30
Last Week
0
Last month
0
Citations as of Sep 23, 2017

Page view(s)

37
Last Week
3
Last month
Checked on Sep 24, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.