Control System

Control systems research focuses on designing and improving systems that regulate processes to achieve desired outcomes, encompassing diverse applications from robotics and autonomous vehicles to industrial automation and aerospace. Current research emphasizes data-driven approaches, utilizing neural networks (including shallow ReLU networks and spiking neural networks), and integrating federated learning for distributed and privacy-preserving control. These advancements aim to enhance the adaptability, robustness, and efficiency of control systems, particularly in complex and resource-constrained environments, while addressing challenges like safety verification and real-time performance.