Data-Driven Modeling, Prediction and Control of Fractional-Order Systems

Dear Colleagues,

Over the past few decades, the modeling, analysis, and control of fractional-order systems have attracted considerable attention and shown the increasingly important role of fractional calculus in various science and engineering fields. Fractional-order systems are believed to offer significant advantages over integer-order dynamics in our real-world applications. Moreover, as is well-known, data-driven discovery has revolutionized how we model, predict, and control complex systems. With modern mathematical methods, enabled by the unprecedented availability of data and computational resources, data-driven modeling, prediction, and control of fractional-order systems, can then allow us to obtain more adaptive, efficient, and intelligent results compared to the available studies and to also tackle previously unattainable problems. A collocation of the recent developments on the modeling, analysis, and control methods of fractional-order systems and their applications would be both challenging and significant.

The aim of this Special Issue is to show the control engineering research community the usefulness of the data-driven discovery of fractional-order systems, ranging from modeling to control prediction. It is our sincere hope that this Special Issue can show the important theoretical significance and practical values of data-driven modeling, prediction, and control of complex systems, and inspire further research of this topic.

Topics of this Special Issue include, but are not limited to, the following:

• Data-driven modeling and identification for fractional-order ODE/PDE systems;
• Data-driven learning and prediction of fractional-order ODE/PDE systems;
• Data-driven controller design for fractional-order ODE/PDE systems;
• Data-driven optimal control of fractional-order ODE/PDE systems;
• The applications of advanced fractional data-driven modeling, prediction, and control methods in climate, epidemiology, finance, robotics, turbulence, etc.

We look forward to your contributions.

Dr. Fudong Ge
Dr. Ying Luo
Prof. Dr. Yangquan Chen
Guest Editors

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