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Adaptive, Cautious, Predictive control with Gaussian Process Priors
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.
@inproceedings{2316, title = {Adaptive, Cautious, Predictive control with Gaussian Process Priors}, journal = {Proceedings of the 13th IFAC Symposium on System Identification}, 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.}, pages = {1195-1200}, editors = {Van den Hof, P., B. Wahlberg and S. Weiland}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = aug, year = {2003}, slug = {2316}, author = {Murray-Smith, R. and Sbarbaro, D. and Rasmussen, CE. and Girard, A.}, month_numeric = {8} }