文章基本信息

标题：Stochastic Model Predictive Control with Adaptive Chance Constraints based on empirical Cumulative Distributions ⁎
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作者：Tito L.M. Santos ; Victor M. Cunha ; Ali Mesbah 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：11257-11263
DOI：10.1016/j.ifacol.2020.12.359
语种：English
出版社：Elsevier
摘要：AbstractIndividual chance constraints can be used to systematically seek trade-offs between control performance and constraint violation for a given disturbance description. This paper presents a Stochastic Model Predictive Control (SMPC) approach with adaptive individual chance constraints that relies on online adaptation of the disturbance description using the empirical cumulative distribution (ECDF). Individual chance constraints are ensured by using a suitable worst-case confidence interval derived from the ECDF. The confidence interval may, however, be excessively conservative due to the empirical nature of the ECDF. To reduce this conservatism, the proposed approach accounts for the updated disturbance information, which is sampled online from the one-step ahead prediction error. Hence, the initial ECDF can be obtained from a reduced number of samples since the conservative handling of the chance constraint is continuously mitigated. This will also allow for using simpler models of the stochastic system disturbances. Convergence and recursive feasibility of the proposed adaptive approach are established. A DC-DC converter benchmark problem is used to illustrate the usefulness of the proposed approach.
关键词：KeywordsStochastic MPCTube-based MPCOffset-free trackingChance Constraintsempirical Distributions