Efficient Control

Efficient control research aims to develop methods for controlling systems using minimal resources, whether computational, data-driven, or sensor-based. Current efforts focus on leveraging neural networks (including spiking neural networks and Koopman operators), reinforcement learning (particularly off-policy and continuous control approaches), and model predictive control, often combined with techniques like feature selection and efficient representation learning to reduce dimensionality and improve performance. These advancements are significant for various applications, including robotics, recommender systems, traffic management, and power grid stabilization, by enabling more robust, adaptable, and resource-efficient control strategies in complex systems.