<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>6977</REFNUM><AUTHORS><AUTHOR>Murray-Smith,R.</AUTHOR><AUTHOR>Sbarbaro,D.</AUTHOR><AUTHOR>Rasmussen,C.E.</AUTHOR><AUTHOR>Girard,A.</AUTHOR></AUTHORS><YEAR>2003</YEAR><TITLE>Adaptive, Cautious, Predictive control with Gaussian Process priors</TITLE><PLACE_PUBLISHED>International Symposium on System Identification </PLACE_PUBLISHED><PUBLISHER>IFAC</PUBLISHER><LABEL>Murray-Smith:2003:6977</LABEL><KEYWORDS><KEYWORD>Cautious control</KEYWORD></KEYWORDS<ABSTRACT> Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example. </ABSTRACT></RECORD></RECORDS></XML>