文章基本信息

标题：Robust Adaptive Model Predictive Building Climate Control
本地全文：下载
作者：Marko Tanaskovic ; David Sturzenegger ; Roy Smith 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：1871-1876
DOI：10.1016/j.ifacol.2017.08.257
语种：English
出版社：Elsevier
摘要：AbstractModel Predictive Control (MPC) is a promising algorithm for building climate control. However, the construction of an accurate building model is very challenging since identification experiments on buildings are, due to time and usage constraints, difficult to conduct and the construction data are often only known approximately. This paper proposes the use of a recently developed robust, adaptive MPC as a possible solution for handling this model uncertainty. Starting from an initial set of possible models, the algorithm relies on recursive set membership identification to shrink this set in each step of the closed-loop operation. The MPC enforces comfort constraints for all the models in the current set and hence achieves robust constraint satisfaction even during the adaptation phase. The proposed algorithm is compared to a standard, non-robust, non-adaptive MPC and to a non-robust, adaptive MPC in simulations using the EnergyPlus building simulation software.
关键词：KeywordsBuilding controladaptive MPClearning controlimpulse response