Model predictive control of room temperature with disturbance compensation

[1] A. Marvuglia, A. Messineo and G. Nicolosi, ”Coupling a neural network temperature predictor and a fuzzy logic controller to perform thermal comfort regulation an office building”, Building and Environment, vol. 72, 2014, pp. 287-299.10.1016/j.buildenv.2013.10.020Search in Google Scholar

[2] Mohamed, Mahmoud and Gouda, ”Fuzzy Ventilation Control for Zone Temperature and Relative Humidity ”, American Control Conference, Portland, 2005.Search in Google Scholar

[3] M. Mossolly, K. Ghali and N. Ghaddar, ”Optimal control strat- egy for a multi-zone air conditioning system using genetic algo- rithm”, Energy, vol. 34, 2009, pp. 58-66.10.1016/j.energy.2008.10.001Search in Google Scholar

[4] A. Pawlowski, J. L. Guzmán, J. E. Normey-Rico and M. Beren- guel, ”Improving feedforward disturbance compensation capabil- ities Generalized Predictive Control”, Journal of Process Con- trol, vol. 22, 2012, pp. 527-539.10.1016/j.jprocont.2012.01.010Search in Google Scholar

[5] P. Morosan, R. Bourdaisa, D. Dumurb and J. Buissona, ”Build- ing temperature regulation using a distributed model predictive control”, American Control Conference, Baltimore, 2010.Search in Google Scholar

[6] R. Holiš, V and Bobál, ”Linear pedictive control of nonlinear time-delayed model of liquid-liquid stirred heat exchanger”, Eu- ropeanCouncil for Modeling and Simulation, Regensburg, Ger- many, 2016.10.7148/2016-0300Search in Google Scholar

[7] E. F. Camacho and C. Bordons, ””, Model Predictive Control, London, Springer, 2004.Search in Google Scholar

[8] P. Morosan, ”Distributed predictive control, Approaches to ther- mal regulation of buildings”, Dissertation thesis, Suplec, France, 2011.French.Search in Google Scholar

[9] J. Škultéty, ”Predictive control by considering limitations of input and output variables”, Master thesis, Slovak University of Technology Bratislava, Slovakia, 2010, Slovak.Search in Google Scholar