Control Synthesis

Control synthesis focuses on automatically designing controllers that achieve desired system behaviors, often specified using formal languages like linear temporal logic (LTL) or signal temporal logic (STL). Current research emphasizes robust and safe control synthesis, particularly for complex systems like multi-robot teams and autonomous vehicles, employing diverse methods including model predictive control (MPC), reinforcement learning (RL), and neural networks (NNs) – often combined with techniques like control barrier functions (CBFs) for safety guarantees. These advancements are crucial for deploying reliable and safe autonomous systems in various domains, from manufacturing to robotics and transportation.