文章基本信息

标题：Effective Recursive Set-membership State Estimation for Robust Linear MPC
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作者：Carlos E. Valero ; Radoslav Paulen
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：1
页码：486-491
DOI：10.1016/j.ifacol.2019.06.109
语种：English
出版社：Elsevier
摘要：AbstractIn this paper the problem of robust output-feedback Model Predictive Control (MPC) is considered. Uncertainty in the state estimates obtained from noisy measurements is bounded using set-membership techniques as we consider the noise to be bounded. Robustness of the MPC controller is achieved in a min-max sense. We use parallelotopic bounding for the state estimates. We propose enhancements to a well-known Recursive Optimal Parallelotopic Outbounding (ROPO) algorithm such that the resulting closed-loop cost is improved. All methodologies are tested using a simple linear case study. The results obtained show the benefits of the developed methods.
关键词：KeywordsPredictive controlBounded noiseState estimation