Stochastic Disturbance

Stochastic disturbance, encompassing unpredictable variations in system dynamics, is a central challenge across numerous scientific and engineering domains, with research focused on mitigating its effects on system performance and stability. Current efforts employ diverse approaches, including data-driven methods like reinforcement learning and kernel methods, recursive online estimation algorithms, and distributionally robust control strategies that account for uncertainty in disturbance characteristics. These advancements are crucial for improving the robustness and reliability of control systems in applications ranging from robotics and power systems to transportation networks and chaotic systems.