<XML><RECORDS><RECORD><REFERENCE_TYPE>7</REFERENCE_TYPE><REFNUM>7898</REFNUM><AUTHORS><AUTHOR>Kocijan,J.</AUTHOR><AUTHOR>Murray-Smith,R.</AUTHOR></AUTHORS><YEAR>2005</YEAR><TITLE>Nonlinear Predictive Control with Gaussian Process Model</TITLE><PLACE_PUBLISHED>Proceedings of the Hamilton Summer School on Switching and Learning in Feedback systems, Ed. R. Murray-Smith, R. Shorten, Springer-Verlag, Lecture Notes in Computing Science, Vol. 3355 </PLACE_PUBLISHED><PUBLISHER>Springer Verlag</PUBLISHER><PAGES>p185-200</PAGES><LABEL>Kocijan:2005:7898</LABEL><ABSTRACT>Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This chapter illustrates possible application of Gaussian process models within model predictive control. The extra information provided by the Gaussian process model is used in predictive control, where optimization of the control signal takes the variance information into account. The predictive control principle is demonstrated via the control of a pH process benchmark. </ABSTRACT></RECORD></RECORDS></XML>