Control Design

Control design focuses on creating algorithms that govern the behavior of dynamic systems, aiming for stability, safety, and optimal performance. Current research emphasizes data-driven approaches, utilizing machine learning techniques like reinforcement learning, neural networks (including recurrent and autoencoder architectures), and Bayesian methods to design controllers for complex, often nonlinear systems, with a growing focus on handling uncertainties and constraints. These advancements are crucial for improving the efficiency and reliability of various applications, from robotics and autonomous vehicles to power plants and medical devices.