Performance-enhanced iterative learning control using a model-free disturbance observer

Abstract

This paper proposes a novel performance-enhanced iterative learning control (ILC) scheme using a model-free disturbance observer (DOB) to achieve high performance for precision motion systems that encounter non-repetitive disturbances. As is well known, the performance of the standard ILC (SILC) is severely degraded by the non-repetitive disturbances. By introducing DOB into SILC, this paper improves the robustness of the ILC system against non-repetitive disturbances. In the proposed enhanced ILC (EILC), SILC aims at learning the feedforward signals for a specific reference, while DOB is to compensate for external disturbances. Little or no plant model knowledge is required for SILC. To maintain this advantage after introducing DOB, a model-free design method for DOB is proposed to release the need for the plant model. Based only on a specific reference and the corresponding feedforward signals learned by SILC, the filter of DOB is optimized via an instrumental-variable estimate method. Numerical simulation is performed to illustrate the effectiveness and enhanced performance of the proposed control approach.