Article ID Journal Published Year Pages File Type
708796 IFAC-PapersOnLine 2016 6 Pages PDF
Abstract

Combining iterative learning with model predictive controllers is reasonable in applications where approximative models for the systems dynamics are available and relevant disturbances are repetitive, e.g. the outside temperature and the heat demand for heating systems. This paper shows how this combined control concept can be designed with a data-driven learning part, because for a rising number of application, signal histories are stored in databases. Simulation results for a heating system of a non-residential building are presented, which show the applicability of the approach.

Related Topics
Physical Sciences and Engineering Engineering Computational Mechanics
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