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|Title:||Support vector regression model predictive control on a HVAC plant||Authors:||Xi, X.-C.
|Keywords:||HVAC (heating, ventilation and air-conditioning)
Nonlinear model predictive control
Support vector regression
|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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