Control Architecture

Control architecture research focuses on designing and implementing effective control systems for complex dynamic systems, aiming for robust performance, stability, and safety. Current efforts emphasize modularity, enabling easier system design, reuse of components, and improved adaptability to changing conditions; popular approaches include reinforcement learning, model predictive control, and adaptive control algorithms, often integrated with passivity-based techniques for safe human-robot interaction. These advancements have significant implications across diverse fields, including robotics (surgical, mobile, and soft robots), autonomous vehicles, and biomedical engineering, improving the reliability and capabilities of these systems.