文章基本信息

标题：Trajectory Bounds of Input-to-State Stability for Nonlinear Model Predictive Control ∗
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作者：Devin W. Griffith ; Lorenz T. Biegler
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：8
页码：1027-1032
DOI：10.1016/j.ifacol.2015.09.103
语种：English
出版社：Elsevier
摘要：AbstractModel predictive control (MPC) is an optimization-based tool that is widely used in the chemical industry, and nonlinear MPC (NMPC) expands the technology to handle more detailed models that are accurate across a wider range of state values. Many works in the literature have studied NMPC using Input-to-State Stability (ISS). The purpose of this work is to provide a method for calculating state trajectory bounds for NMPC using ISS theory. These predictive bounds are derived in terms of parameters that may be found from a series of open loop calculations in the general nonlinear case. Results are shown for a scalar linear system and a nonlinear CSTR, and the challenges involved with higher dimensional problems are discussed.
关键词：Keywordsoptimal controlnonlinear programmingmodel-based controlrobust stabilitymultivariable feedback controlpredictive control