文章基本信息

标题：Constrained Iterative Learning Control with PSO-Youla Feedback Tuning for Building Temperature Control * * This work was supported by the National Foundation of Research (NRF) of Singapore and China Scholarship Council (CSC) Scholarship.
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作者：Cheng Peng ; Liting Sun ; Masayoshi Tomizuka 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：3135-3141
DOI：10.1016/j.ifacol.2017.08.322
语种：English
出版社：Elsevier
摘要：AbstractIn building temperature control, one of the main challenges is to maintain good performance in the presence of disturbances from both outdoor weather and indoor human activities. Considering the fact that such disturbances contain both repetitive patterns and non-repetitive uncertainties, this paper proposes a two-degree-of-freedom (2-DOF) iterative control algorithm that synthesizes iterative learning control (ILC) and iterative feedback tuning (IFT) together. Based on lifting technique and Youla-parametrization, the 2-DOF controller design, i.e., the design of ILC and IFT, is formulated into one constrained optimization problem where an optimal combination of the learning matrix and feedback controller is found in each iteration with guaranteed stability. To make the synthesized problem solvable, techniques such as stabilizing projection and particle swarm optimization (PSO) are utilized in finding the optimal solutions. The effectiveness of the proposed approach is verified by simulations on a four-room building control testbed system.
关键词：KeywordsIterative learning controlIterative feedback tuningConstrained optimizationYoula-parametrizationBuilding temperature control