文章基本信息

标题：Adaptive Model Predictive Control with Robust Constraint Satisfaction
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作者：Matthias Lorenzen ; Frank Allgöwer ; Mark Cannon 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：3313-3318
DOI：10.1016/j.ifacol.2017.08.512
语种：English
出版社：Elsevier
摘要：AbstractAdaptive control for constrained, linear systems is addressed and a solution based on Model Predictive Control (MPC) and set-membership system identification is presented. The paper introduces a computationally tractable solution which uses observations of past state and input trajectories to update the model and improve control performance while maintaining guaranteed constraint satisfaction and recursive feasibility. The developed approach is applied to a stabilizing MPC scheme and practical stability under persistent, additive disturbance is proved. A numerical example and brief comparison with non-adaptive MPC is provided.
关键词：KeywordsModel Predictive ControlAdaptive ControlConstraint Satisfaction ProblemsUncertain Linear SystemsSystem Identification