文章基本信息

标题：Robust Data-Based Model Predictive Control for Nonlinear Constrained Systems ⁎
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作者：J.M. Manzano ; D. Limon ; D. Muñz de la Peñ 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：20
页码：505-510
DOI：10.1016/j.ifacol.2018.11.039
语种：English
出版社：Elsevier
摘要：AbstractThis paper presents stabilizing Model Predictive Controllers (MPC) to be applied to black-box systems subject to constraints in the inputs and the outputs. The prediction model of the controllers is inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called SPKI, the estimated (possibly nonlinear) model function is provided. Based on this, a predictive controller with stability guaranteed by design is proposed. Robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem but without adding a terminal constraint on the optimisation problem. The proposed predictive controller has been validated in a simulation case study.
关键词：KeywordsMPCData-based controlMachine learningInput-to-state stability