文章基本信息

标题：Uncertainty Propagation by Linear Regression Kalman Filters for Stochastic NMPC
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作者：Rien Quirynen ; Karl Berntorp
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：6
页码：76-82
DOI：10.1016/j.ifacol.2021.08.527
语种：English
出版社：Elsevier
摘要：AbstractStochastic nonlinear model predictive control (SNMPC) allows to directly take model uncertainty into account, e.g., by including probabilistic chance constraints. This paper proposes linear-regression Kalman filtering to perform high-accuracy propagation of mean and covariance information for the nonlinear system dynamics in a tractable approximation of the stochastic optimal control problem. In addition, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is presented to considerably reduce the computational cost and allow a real-time implementation of the resulting SNMPC. The prediction accuracy and control performance of the proposed approach are illustrated on a vehicle control application subject to external disturbances, while highlighting a worst-case computation time of 10 ms for SNMPC which is close to that of deterministic NMPC for this particular case study.
关键词：KeywordsStochastic nonlinear model predictive controllinear-regression Kalman filteringsequential quadratic programmingreal-time optimization algorithms