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• W2989668675 abstract "One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive prediction models can be used to overcome this issue and provide predictions of these signals to the controller. In this work we propose to learn references via Gaussian processes for model predictive controllers. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator needs to follow a learned reference position based on optical tracking measurements." @default.
• W2989668675 created "2019-12-05" @default.
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• W2989668675 date "2019-11-25" @default.
• W2989668675 modified "2023-09-27" @default.
• W2989668675 title "Learning References with Gaussian Processes in Model Predictive Control applied to Robot Assisted Surgery" @default.
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• W2989668675 hasPublicationYear "2019" @default.
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