Data-driven Iterative Tuning for Rejecting Spatial Periodic Disturbances Combined with LESO

Iterative learning control (ILC) scheme is known as an effective technique focused on problems which involve repeating tasks, using the error signal from the previous cycle to update the control input. In this paper, a compound control which combines a data-driven iterative turning feedforward controller with a linear extended state observer (LESO) is proposed for spatial periodic disturbances suppression. Due to the problem of feedforward parameter identification in servo system, an algorithm of orthogonal projection is introduced. The error signals caused by the reference trajectory and the disturbances are extracted by projecting the overall error signals onto a subspace spanned by the physical model of the plant as well as the model of the disturbances. Moreover, a data-driven approach is proposed to design the learning gain. Furthermore, a 4th-order LESO is designed to estimate non-periodic disturbances and uncertain dynamics so as to reduce the steady state error. Simulation results validate the proposed method and confirm its effectiveness and superiority.