2019, Volume 20, Issue 1
Frontiers of Information Technology & Electronic Engineering >> 2019, Volume 20, Issue 1 doi: 10.1631/FITEE.1800558
Disturbance rejection via iterative learning controlwith a disturbance observer for active magnetic bearing systems
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Abstract
Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.
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