Research explores reduced-order and decomposition methods for improving identification and estimation, highlighting data utilization in control models.
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.
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Alessandri et al. (2025) studied this question.
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