Spatial Incremental Model-Predictive Repetitive Control for Rotational Systems

This paper presents a spatial repetitive control method for precisely tracking a spatial periodic reference signal, achieved through incremental model-predictive control and an exact-feedback linearization method (IMPSRC) for rotational systems with disturbances. A modeling method in the spatial domain is first presented, followed by the configuration of the IMPSRC system. An exact-feedback-linearization controller is then designed to linearize the system model. Then we built an augmented system model using the dynamics of the tracking error of a spatial-domain periodic reference signal and the difference in the control input between two adjacent periods. The increment of the control input incorporates the internal model of the periodic reference signal, enabling the system to achieve tracking without steady-state error. An incremental model-predictive repetitive controller is then designed based on the augmented model. It enables precisely tracking spatial periodic signals and ensures stability under uncertainties and disturbances. The IMPSRC method is validated through simulations of tracking control of a rotational system. Spatial-domain sampling is triggered by the angular signal of the rotational system, which facilitates the implementation of the IMPSRC method. Simulation results show the effectiveness of the IMPSRC method.

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Publication Details

Journal
Journal of Advanced Computational Intelligence and Intelligent Informatics
Published
2025-09-19
DOI
https://doi.org/10.20965/jaciii.2025.p1047
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Spatial Incremental Model-Predictive Repetitive Control for Rotational Systems

Seiichi Kawata, Makoto Iwasaki, Yujian Zhou, Feng Wang et al.
Journal of Advanced Computational Intelligence and Intelligent Informatics
Iterative Learning Control Systems
article

Spatial Incremental Model-Predictive Repetitive Control for Rotational Systems

Seiichi Kawata, Makoto Iwasaki, Yujian Zhou, Feng Wang, Shijie Guo, Jinhua She
article en

Abstract

This paper presents a spatial repetitive control method for precisely tracking a spatial periodic reference signal, achieved through incremental model-predictive control and an exact-feedback linearization method (IMPSRC) for rotational systems with disturbances. A modeling method in the spatial domain is first presented, followed by the configuration of the IMPSRC system. An exact-feedback-linearization controller is then designed to linearize the system model. Then we built an augmented system model using the dynamics of the tracking error of a spatial-domain periodic reference signal and the difference in the control input between two adjacent periods. The increment of the control input incorporates the internal model of the periodic reference signal, enabling the system to achieve tracking without steady-state error. An incremental model-predictive repetitive controller is then designed based on the augmented model. It enables precisely tracking spatial periodic signals and ensures stability under uncertainties and disturbances. The IMPSRC method is validated through simulations of tracking control of a rotational system. Spatial-domain sampling is triggered by the angular signal of the rotational system, which facilitates the implementation of the IMPSRC method. Simulation results show the effectiveness of the IMPSRC method.

Journal of Advanced Computational Intelligence and Intelligent InformaticsVol. 29(5)
Ministry of Education of the People's Republic of China (CN), Tokyo University of Technology (JP), Hebei University of Technology (CN), Nagoya Institute of Technology (JP), China University of Geosciences (CN), Shandong Institute of Automation (CN)
Openalex Percentile: Top 68%
Iterative Learning Control Systems
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Spatial Incremental Model-Predictive Repetitive Control for Rotational Systems — Seiichi Kawata, Makoto Iwasaki, et al. · Journal of Advanced Computational Intelligence and Intelligent Informatics (2025) | TGRS Research Map | TGRS