Advanced Control Systems: Theory and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "C2: Dynamical Systems".
Deadline for manuscript submissions: 31 August 2026
Special Issue Editors
Interests: nonlinear systems and control; discrete and hybrid control; analysis, identification and control of biomedical systems; control applications; dynamic sensor networks; epidemic modelling and control; optimal control for resource management; sampled data systems
Interests: analysis, identification and control of biomedical systems; epidemic modelling and control; optimal control for resource management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The field of control engineering is undergoing a notable transformation as data becomes increasingly central to both modelling and decision-making. Classical control design traditionally relies on mathematical models derived from physical laws or dynamical relationships, but this paradigm proves limited when systems are highly nonlinear, uncertain, or too complex to describe analytically. To address these challenges, new methodologies based on data processing, machine learning and model predictive approaches have emerged in recent years, offering flexible alternatives to purely model-based approaches.
Data-driven control constitutes one of the most significant developments. By exploiting experimental or operational data, it allows the design of controllers without requiring an exact model of the plant, thus adapting directly to observed behaviour. In parallel, predictive control frameworks are being reformulated with machine learning models such as recurrent neural networks, which can forecast system behaviour and optimize performances even without the necessity of accurate mathematical modelling. More broadly, machine learning enhances control through supervised methods for state estimation or disturbance prediction, and through reinforcement learning, which enables controllers to learn optimal strategies via interaction with the environment.
This Special Issue aims to serve as a venue for collecting such novel approaches that extend the boundaries of modelling and control design across a rich and heterogeneous range of applications, providing a comprehensive overview and a valuable reference for future work.
Prof. Dr. Paolo Di Giamberardino and Daniela Iacoviello
Prof. Dr. Paolo Di Giamberardino
Dr. Daniela Iacoviello
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- advanced control
- machine learning modelling
- data-driven control
- model predictive
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