Our research group is primarily focused on nonlinear control systems. Over the years, we have made significant contributions to the development of constructive algebraic methods that offer a unified approach to analyzing diverse challenges in nonlinear control. While our work is largely centered on general, application-independent methodologies grounded in the dynamic properties of mathematical models, we have recently begun targeting specific application domains such as robotic systems and smart buildings.
A growing area of emphasis is the study of real-world problems at the intersection of renewable energy integration and distributed energy storage in low-inertia power systems. Leveraging tools from optimal control theory, we aim to identify and overcome fundamental limitations in these emerging energy infrastructures.
In parallel, the group is increasingly integrating techniques from data science, machine learning, and energy informatics to enhance decision-making and system performance. We remain committed to advancing the role of information technology in the energy sector and promoting its widespread adoption for a more efficient, intelligent, and sustainable energy future.
