Robust Control

Robust control focuses on designing control systems that maintain stability and performance despite uncertainties in the system model or external disturbances. Current research emphasizes data-driven approaches, leveraging machine learning techniques like reinforcement learning and neural networks (including Koopman operators and Taylor-neural Lyapunov functions) to learn robust controllers and estimate uncertainty sets, often within model predictive control frameworks. These advancements are crucial for enhancing the reliability and safety of autonomous systems across diverse applications, from robotics and aerospace to bioprocess control and medical devices.