Support vector regression model predictive control on a HVAC plant

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.conengprac.2006.10.010

Title:	Support vector regression model predictive control on a HVAC plant
Authors:	Xi, X.-C. Poo, A.-N. Chou, S.-K.
Keywords:	HVAC (heating, ventilation and air-conditioning) Modeling Nonlinear model predictive control Relative humidity Support vector regression Temperature
Issue Date:	Aug-2007
Citation:	Xi, X.-C., Poo, A.-N., Chou, S.-K. (2007-08). Support vector regression model predictive control on a HVAC plant. Control Engineering Practice 15 (8) : 897-908. ScholarBank@NUS Repository. https://doi.org/10.1016/j.conengprac.2006.10.010
Abstract:	Some industrial and scientific processes require simultaneous and accurate control of temperature and relative humidity. In this paper, support vector regression (SVR) is used to build the 2-by-2 nonlinear dynamic model of a HVAC system. A nonlinear model predictive controller is then designed based on this model and an optimization algorithm is used to generate online the control signals within the control constraints. Experimental results show good control performance in terms of reference command tracking ability and steady-state errors. This performance is superior to that obtained using a neural fuzzy controller. © 2007.
Source Title:	Control Engineering Practice
URI:	http://scholarbank.nus.edu.sg/handle/10635/61422
ISSN:	09670661
DOI:	10.1016/j.conengprac.2006.10.010
Appears in Collections:	Staff Publications

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