The Nonlinear Control Systems group is a leading Estonian research unit in automatic control, focusing on learning-enabled and data-driven control of complex energy systems. Its work builds on a strong foundation in nonlinear control theory, including non-smooth, hybrid, and time-delay systems, while increasingly integrating machine learning and optimization methods.
The group develops algorithms for trustworthy control of cyber-physical systems, with emphasis on transparency, robustness, and safety guarantees. Current research focuses on energy systems and the built environment, including intelligent building control, demand-side flexibility, and integration of renewable energy sources, while bridging machine learning and control engineering through explainability-aware learning, supervisory safety mechanisms, and data-centric design approaches.
Through interdisciplinary collaboration, the group contributes to the emerging field of energy informatics, addressing challenges in digitalization, sustainability, and reliable operation of modern energy systems.
Research Directions
• nonlinear and hybrid control systems
• data-driven control
• explainable and trustworthy AI for control
• modeling and control of energy and building systems
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