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Reduced-Order, Data-Driven, and Decomposition Methods for Modelling, Identification, and Estimation
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DOI：10.1002/acs.4063.png)
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Abstract
En 中文
The focus of this special issue is on research applying reduced-order, data-driven, and decomposition paradigms within the control community. The issue includes six papers that address two main types of models: black-box ones, such as neural networks and multivariate splines, and classical white-box ones. Regardless of the specific modeling framework, the proposed solutions demonstrate how to leverage the available data from the plant of interest. This aspect, which is shared among the various approaches, aims to enhance experimental performance in identification, estimation, and control, while aligning with the underlying theoretical foundations.
Keywords:
approximation
data-driven modeling
dynamic mode decomposition
filtering
neural learning
reduced-order modeling
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