文章基本信息

标题：A Deep Learning Architecture for Predictive Control
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作者：Steven Spielberg Pon Kumar ; Aditya Tulsyan ; Bhushan Gopaluni 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：512-517
DOI：10.1016/j.ifacol.2018.09.373
语种：English
出版社：Elsevier
摘要：AbstractModel predictive control (MPC) is a popular control strategy that computes control actions by solving an optimization problem in real-time. Uncertainty and nonlinearity of a process, and the non-convexity of the resulting optimization problem can make online implementation of MPC nontrivial. Consequently, MPC is most often used in processes where the time constants are large and/or high-performance computing support is available. We propose a deep neural network (DNN) controller architecture to reduce the computational cost of implementing an MPC. This is done by training a DNN controller on simulated input-output data from a well-designed MPC. The online implementation of a DNN controller does not require solving an optimization problem. Once the DNN is trained, the MPC is fully replaced with the DNN controller. The benefits of this approach are illustrated through a simulated example.
关键词：Keywordsartificial intelligencemodel predictive controldeep neural networksoptimization