Data-Driven Control: Overview and Perspectives *
Data-Driven Control: Overview and Perspectives *
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DOI：
10.23919/acc53348.2022.9867266
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Wentao Tang;P. Daoutidis
中科院分区：
文献类型：
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作者：
Wentao Tang;P. Daoutidis
过程系统具有非线性、不确定性、大规模等特点，并且需要在运行过程中追求安全性和经济性的最优性。因此，他们很难有效地控制。数据驱动技术，如机器学习算法，可以通过增强复杂系统动态建模和控制性能维护的能力，为经典的基于模型的控制提供补充工具和见解。此外，通过将植物和控制器的行为学习为黑盒，数据驱动技术可以实现完全无模型的控制范式。因此，数据驱动的过程控制有可能减轻最先进控制技术的挑战，并产生通用的、自适应的和可扩展的策略。本文旨在对这一新兴领域的主要方法进行概述和概念分类，并确定当前的局限性和未来的方向。
Process systems are characterized by nonlinearity, uncertainty, large scales, and also the need of pursuing both safety and economic optimality in operations. As a result they are difficult to control effectively. Data-driven techniques such as machine learning algorithms can provide complementary tools and insights to classical model-based control by enhancing the capability of modeling the dynamics of complex systems and the maintenance of control performance. Moreover, by learning the behavior of plants and controllers as black boxes, data-driven techniques can enable a completely model-free control paradigm. Hence, data-driven process control has the potential to mitigate the challenges of state-of-the-art control technology and yield generic, adaptive, and scalable strategies. This paper aims at providing an overview and conceptual classification of the main approaches in this emerging and promising field, and identifying current limitations and future directions.