文章基本信息

标题：NMPC Through qLPV Embedding: A Tutorial Review of Different Approaches ⁎
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作者：Marcelo M. Morato ; Gia Quoc Bao Tran ; Guilherme N.G. dos Reis 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：6
页码：302-307
DOI：10.1016/j.ifacol.2021.08.561
语种：English
出版社：Elsevier
摘要：AbstractNonlinear Model Predictive Control (NMPC) formulations through quasi-Linear Parameter Varying (qLPV) embeddings have been brought to focus in recent literature. The qLPV realisation of the nonlinear dynamics yields linear predictions at each sampling instant. Thereby, these strategies generate online programs with reduced numerical burden, much faster to solve than the Nonlinear Programs generated with regular NMPC. The general lines of these methods:(i)The qLPV embedding is formulated with state-dependent scheduling parameters;(ii)Recursive extrapolation procedures are used to estimate the values of these parameters along the prediction horizon;(iii)These estimates are used to compute linear predictions, which are incorporated by the constrained optimisation procedure. This paper details the overall concept of these novel NMPC techniques and reviews two different (efficient) implementation options. Realistic academic examples are provided to illustrate their performances.
关键词：KeywordsNonlinear Model Predictive ControlLinear Parameter Varying SystemsTutorial