Disturbance rejection based on iterative learning control with extended state observer for a four-degree-of-freedom hybrid magnetic bearing system

Abstract

Iterative learning control (ILC) is an iterative control strategy which calculates a new input according to the error in previous cycles. It is widely used in industries with repetitive operations. Since magnetic bearing systems can be considered as running in a repetitive task, the ILC could be applied. Hence, this paper proposes a modified iterative learning control (ILC) strategy for a four degree-of-freedom (DOF) hybrid magnetic bearing system to reject the disturbance based on an extended state observer (ESO). The feasibility of applying the ILC to the four-DOF magnetic bearing system is analyzed firstly. It is proved that the tracking error can converge by selecting suitable controller parameters. Secondly, to accurately obtain the uncertainties in the operation process, an ESO is designed. As for a repetitive rotation process, the iteration variant disturbance may have a certain influence on the performance of the system which is not usually considered. Therefore, the iteration variant disturbance is introduced and the effectiveness is derived. Finally, simulations and experiments are carried out to demonstrate the effectiveness of the proposed method. The classical proportion-integration-differentiation (PID) control and an existing proposed neural network inverse (NNI) control are implemented for comparison. The results show that the proposed strategy can achieve better reference tracking and disturbance suppression ability than PID and NNI control.