<XML><RECORDS><RECORD><REFERENCE_TYPE>10</REFERENCE_TYPE><REFNUM>6991</REFNUM><AUTHORS><AUTHOR>Sbarbaro,D.</AUTHOR><AUTHOR>Murray-Smith,R.</AUTHOR></AUTHORS><YEAR>2003</YEAR><TITLE>Self-tuning control of non-linear systems using Gaussian process prior models</TITLE><PLACE_PUBLISHED>DCS Tech Report</PLACE_PUBLISHED><PUBLISHER>N/A</PUBLISHER><ISBN>TR-2003-143</ISBN><LABEL>Sbarbaro:2003:6991</LABEL><KEYWORDS><KEYWORD>Gaussian process prior</KEYWORD></KEYWORDS<ABSTRACT>Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a quadratic cost function is minimised, without ignoring the variance of the model predictions. This leads to implicit regularisation of the control signal(caution) in areas of high uncertainty. As a consequence, the controller has dual features, since it both tracks a reference signal and learns a model of the system from observed responses. The general method and its main features are illustrated on simulation examples. </ABSTRACT></RECORD></RECORDS></XML>