Ergodic Control

Ergodic control focuses on designing control strategies that ensure a system's long-term average behavior matches a desired target distribution, effectively steering a system towards a specific statistical equilibrium. Current research emphasizes computational methods, often leveraging neural networks or spectral decompositions, to achieve ergodic control in high-dimensional systems, including robotic swarms and complex queueing networks. These advancements are significant for applications ranging from optimizing robotic exploration and manipulation to managing large-scale systems, offering improved efficiency and robustness in diverse domains.