Dynamic systems can often be described using algebraic and/or differential equations derived from first principles. We are interested in describing behaviors of various systems using available data in form of input-output measurements and/or disturbance estimates. Specifically, we focus on developing novel model-free control approaches and benchmarks to test their performance against either existing methods or model-based controllers.

In many real-world applications, stochastic disturbances pose significant challenges, such as distributed energy systems facing uncertain wind speed and renewable energy generation, or building control systems dealing with uncertain weather conditions and occupancy. The presence of stochastic disturbances can severely deteriorate both the performance and safety of the system. However, extending the framework of data-driven control to stochastic systems—where uncertainties must be explicitly accounted for—remains an open problem. In this context, another focus of our work centers on addressing this challenge.