文章基本信息

标题：Fast Nonlinear MPC for Reference Tracking Subject to Nonlinear Constraints via Quasi-LPV Representations
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作者：Pablo Gonzalez Cisneros ; Herbert Werner
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：11601-11606
DOI：10.1016/j.ifacol.2017.08.1650
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents an approach to efficiently implement Nonlinear Model Predictive Control (NMPC) for reference tracking in the presence of nonlinear input and state constraints by making use of quasi-Linear Parameter Varying (quasi-LPV) representations. Using this framework, standard Quadratic Program (QP) solvers can be used for the online optimization problem, making its solution very efficient and viable even for fast plants. This is an extension of a previous result which considered the regulator problem with input constraints. This approach is tested in a simulation study of a 2-DOF robotic manipulator and its efficiency is compared to that of state-of-the-art NMPC approaches.
关键词：KeywordsModel predictive controllinear parameter varying systemsnonlinear systemsconstrained controlefficient algorithms