非线性系统的概率预测方法及其在随机模型预测控制中的应用

IF 7.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Annual Reviews in Control Pub Date : 2023-01-01 DOI:10.1016/j.arcontrol.2023.100905
Daniel Landgraf , Andreas Völz , Felix Berkel , Kevin Schmidt , Thomas Specker , Knut Graichen
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引用次数: 2

摘要

现代控制方法(如模型预测控制)的性能在很大程度上取决于系统模型的准确性。然而,在实践中,由于建模不准确或外部干扰,随机不确定性通常存在,这可能对控制性能产生负面影响。本文综述了非线性系统概率不确定性预测方法的文献。由于在非线性情况下,概率密度函数的精确预测需要很高的计算工作量,因此本文的重点是逼近方法,这些方法在控制工程实践中特别重要。这些方法根据其近似类型以及关于输入和输出分布的假设进行分类。此外,还讨论了这些预测方法在随机模型预测控制中的应用,包括非线性系统的文献综述。最后,对最重要的概率预测方法进行了数值评估。为此,首先研究了这些方法的估计精度,然后使用多个非线性系统,包括自动驾驶汽车的动力学,检查了具有不同预测方法的随机模型预测控制器的性能。
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Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control
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来源期刊
Annual Reviews in Control
Annual Reviews in Control 工程技术-自动化与控制系统
CiteScore
19.00
自引率
2.10%
发文量
53
审稿时长
36 days
期刊介绍: The field of Control is changing very fast now with technology-driven “societal grand challenges” and with the deployment of new digital technologies. The aim of Annual Reviews in Control is to provide comprehensive and visionary views of the field of Control, by publishing the following types of review articles: Survey Article: Review papers on main methodologies or technical advances adding considerable technical value to the state of the art. Note that papers which purely rely on mechanistic searches and lack comprehensive analysis providing a clear contribution to the field will be rejected. Vision Article: Cutting-edge and emerging topics with visionary perspective on the future of the field or how it will bridge multiple disciplines, and Tutorial research Article: Fundamental guides for future studies.
期刊最新文献
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