Discrete Control

Discrete control focuses on designing systems that make decisions at distinct time intervals, optimizing actions and their timing for efficiency and performance. Current research emphasizes developing efficient algorithms, such as those based on reinforcement learning and model predictive control, often incorporating neural networks for system modeling and optimization, particularly within the context of resource-constrained environments. This field is crucial for advancing autonomous systems, improving energy efficiency in buildings and industrial processes, and enabling safer and more robust control in robotics and other applications.