Open AccessPerformance‐enhanced iterative learning control using a model‐free disturbance observer
Author(s) -
Li Min,
Yan Tingjian,
Mao Caohui,
Wen Long,
Zhang Xinxin,
Huang Tao
Publication year - 2021
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12096
Subject(s) - iterative learning control , control theory (sociology) , feed forward , robustness (evolution) , computer science , control engineering , variable (mathematics) , observer (physics) , control (management) , engineering , artificial intelligence , mathematics , mathematical analysis , biochemistry , chemistry , gene , physics , quantum mechanics
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.