Nonlinear Model Predictive Control with Latent Force Models
Author:
Affiliation:
1. Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU),Chair of Automatic Control,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9866948/9867142/09867650.pdf?arnumber=9867650
Reference27 articles.
1. Kalman filtering and smoothing solutions to temporal Gaussian process regression models
2. Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
3. Model predictive control using efficient Gaussian processes for unknown disturbance inputs;graßhoff;Proc of the 58th IEEE Conference on Decision and Control (CDC),2019
4. Bayesian Filtering and Smoothing
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1. Occupancy Prediction for Building Energy Systems with Latent Force Models;Energy and Buildings;2024-02
2. Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control;Annual Reviews in Control;2023
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