Adaptive, Cautious, Predictive control with Gaussian Process Priors

Murray-Smith, R; Sbarbaro, D; Rasmussen, CE; Girard, A

doi:10.1016/S1474-6670(17)34915-7

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Adaptive, Cautious, Predictive control with Gaussian Process Priors

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Rasmussen, CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S1474667017349157
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Citation

Murray-Smith, R., Sbarbaro, D., Rasmussen, C., & Girard, A. (2003). Adaptive, Cautious, Predictive control with Gaussian Process Priors. IFAC Proceedings Volumes, 36(16), 1155-1160.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DBF8-F

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.