Traditional systems and control theory relies on model-based methods, which assume the availability of a precise mathematical model of the system to be controlled. However, as engineering systems grow increasingly complex, deriving accurate mathematical models becomes challenging. Simultaneously, advancements in technology provide unprecedented access to data, making data-driven approaches a transformative alternative to model-based methods.

This course provides a comprehensive introduction to data-driven control, equipping participants with the tools to analyze and design feedback controllers directly from system data. It delves into cutting-edge developments in the field, focusing on the data informativity framework, which determines the conditions under which collected data are sufficient for solving analysis and control problems effectively.

Through this course, participants will learn how to leverage data for control design without the need for explicit mathematical models. Practical case studies and applications will demonstrate how data-driven techniques are applied to address real-world engineering challenges, such as robotics, autonomous systems, and industrial process control.

The course integrates core concepts from system theory, linear algebra, and mathematical programming, ensuring participants gain both theoretical insights and practical skills. Designed for Master's and Ph.D. students in electrical, control, and computer engineering, as well as researchers and professionals, this course prepares attendees to address the challenges of modern, data-rich engineering systems with innovative solutions.

Course participants will gain knowledge through interactive lectures, hands-on experiments, and software simulations using tools like MATLAB, Python, etc. Case studies and real-world applications will further inspire research motivation, ensuring participants are equipped to address contemporary challenges in data-based systems and control.

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