Controller Synthesis

Controller synthesis focuses on automatically designing control algorithms that guarantee desired system behaviors, such as stability and safety, while satisfying given specifications. Current research emphasizes developing methods for handling continuous state and action spaces, often employing techniques like Control Lyapunov and Barrier Functions (CLFs and CBFs), Q-learning with symbolic models, and neural network-based approaches including Decision Transformers. These advancements are crucial for deploying reliable and safe controllers in complex systems, ranging from robotics and autonomous vehicles to aerospace and process control, enabling formal verification and improved performance guarantees.