Disturbance Observer

Disturbance observers (DOBs) are control systems designed to estimate and compensate for unwanted disturbances affecting a system's performance, improving accuracy and robustness. Current research focuses on enhancing DOBs through integration with machine learning techniques like regularized least squares and iterative learning control, particularly for handling coupled disturbances and mismatched dynamics in complex systems such as robots and autonomous vehicles. These advancements are significant for improving the precision and reliability of control systems across various applications, from robotics and autonomous driving to underwater vehicle operation. The development of robust and computationally efficient DOBs is crucial for enabling more sophisticated and reliable control in challenging environments.