Classical Control

Classical control methods, aiming to design systems that achieve desired behaviors, are being enhanced and extended through integration with modern machine learning techniques. Current research focuses on combining classical controllers with reinforcement learning, often using neural networks (including transformers and Gaussian processes) to improve adaptability, robustness, and real-time performance in complex systems like robotic swarms and quadrotor control. This hybrid approach addresses limitations of purely classical or purely learning-based methods, leading to more efficient and reliable control in diverse applications, from robotics and aerospace to network optimization.