文章基本信息

标题：Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty
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作者：Raffaele Soloperto ; Matthias A. Müller ; Sebastian Trimpe 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：20
页码：442-447
DOI：10.1016/j.ifacol.2018.11.052
语种：English
出版社：Elsevier
摘要：AbstractA robust model predictive control (RMPC) approach for linear systems with bounded state-dependent uncertainties is proposed. Such uncertainties can arise from unmodeled non-linearities or external disturbances, for example. By explicitly considering the state dependency of the uncertainty sets in the RMPC approach, it is shown how closed-loop performance can be improved over existing approaches that consider worst-case uncertainty. Being able to handle state-dependent uncertainties is particularly relevant in learning-based MPC where the system model is learned from data and confidence in the model typically varies over the state space. The efficacy of the proposed approach for learning-based RMPC is illustrated with a numerical example, where uncertainty sets are obtained from data using Gaussian Process regression.
关键词：KeywordsRobust MPCLearning-based MPCState-dependent uncertaintyGaussian Process