Behavioral Theory of Systems and Data-Driven Control

Traditional control engineering relies heavily on complex state-space and transfer function models, but modern engineering increasingly demands methods that work directly with raw data. This course introduces you to the behavioral theory of systems, a powerful framework that views systems through their observable trajectories rather than hidden internal states. You will transition from classical model-based control concepts to modern data-driven control techniques. By understanding systems as sets of signals, you will learn how to analyze system properties and perform control design directly from experimental data using foundational principles like Willems' Fundamental Lemma. What you'll learn: - Understand the core principles of behavioral system theory and signal trajectories - Represent dynamical systems using kernel and image representations instead of state-space models - Apply Willems' Fundamental Lemma to characterize system behavior from raw data - Design data-driven control systems directly from experimental trajectory inputs - Analyze system properties such as controllability and observability from a behavioral perspective - Practice implementing basic data-driven simulation and control algorithms through text-based scenarios The course starts with fundamental definitions of signals, behaviors, and representations, before progressing to data-driven analysis and practical control design workflows. You will read detailed explanations and work through conceptual exercises to solidify your understanding of modern control frameworks. This course is designed for beginners in advanced control theory, engineering students, and data scientists looking to bridge the gap between systems theory and data-driven methods, requiring only basic linear algebra. Start exploring the behavioral approach to modern system control today.