Disturbance Rejection

Disturbance rejection in control systems focuses on designing controllers that maintain desired system performance despite unpredictable external forces or internal uncertainties. Current research emphasizes robust control strategies, often employing model predictive control, reinforcement learning (including deep reinforcement learning and actor-critic methods), and sliding mode observers to estimate and compensate for disturbances. These advancements are crucial for improving the reliability and performance of various applications, ranging from autonomous robots and underwater vehicles to aerial drones and industrial manipulators, enabling more robust and adaptable systems in complex environments.