Behavioural Theory of Systems with a View Toward Data Driven Control

This course introduces a modern way of designing control systems using data instead of mathematical models. Traditionally, control design requires building an accurate model of the system using equations derived from physics or experiments. In many real-world systems, this is difficult, time-consuming, or even impossible. Data-driven control overcomes this challenge by directly using measured input–output data.The course begins with the behavioural approach to systems theory, where the focus is on how a system behaves rather than on its internal equations. Students will learn how a system can be described through the set of all possible trajectories it can generate, and why this viewpoint is powerful for data-based methods.Next, the course explains how behavioural theory naturally leads to data-driven control techniques. Students will see how controllers can be designed and system properties can be analyzed using only data collected from experiments, without identifying an explicit model.A key concept covered in detail is persistency of excitation, which explains what kind of data is needed to reliably represent system behavior. The course builds intuition on why rich and informative data is essential for successful control design.By the end of the course, students will understand the fundamental ideas, tools, and limitations of data-driven control, and will be able to appreciate how these methods are applied to modern engineering systems where modeling is difficult or uncertain.

Source: NPTEL IIT Bombay [Youtube Channel]