Combined ILC and Disturbance Observer for the Rejection of Near-Repetitive Disturbances, With Application to Excavation

This paper proposes a new control structure for tasks where explicit disturbance compensation is not only critical for overcoming poor feedback performance but is also challenging due to the complexity and nonrepetitive nature of the interaction between the plant and the environment. The approach proposed uses a particular form of iterative learning control (ILC) to estimate the previous disturbances, which are used as a preview of the disturbance in the next iteration. A disturbance observer is used to compensate for the difference between the ILC prediction and the true disturbance. The controller is evaluated and compared with a proportional controller, with ILC, and with an observer-based controller in extensive field trials using an automated excavator.