IMPC of Multiple Precision Motion Systems Subject to Dynamic Coupling and Input Delay

Complex manufacturing processes, such as lithography, frequently depend on the coordinated operation of multiple precision motion systems whose accuracy is typically undermined by three principal limitations: dynamic coupling among the subsystems, input delays within their control loops and external disturbances present in the control input. The latter, in particular, degrades phase margin and can destabilize high-bandwidth loops. To address these challenges simultaneously, this paper introduces a novel Incremental Model Predictive Control (IMPC) framework, which integrates three key components: (i) a discrete-time state-space model that explicitly represents the coupled dynamics and the input delay, (ii) a robust positively invariant (RPI) set observer for robust state estimation, and (iii) an embedded delay compensation mechanism. Numerical simulations demonstrate that this approach achieves superior trajectory tracking performance and enhanced disturbance rejection compared to conventional approaches, thus effectively mitigating the detrimental effects of dynamic coupling and input delay.

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Publication Details

Journal
Cybernetics & Systems
Published
2026-09-07
DOI
https://doi.org/10.1080/01969722.2026.2727424
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

IMPC of Multiple Precision Motion Systems Subject to Dynamic Coupling and Input Delay

Shuaiqi Chen, Fazhi Song, Kai Zhang, Yang Liu et al.
Cybernetics & Systems
Iterative Learning Control Systems
article

IMPC of Multiple Precision Motion Systems Subject to Dynamic Coupling and Input Delay

Shuaiqi Chen, Fazhi Song, Kai Zhang, Yang Liu, Ning Cui
article en

Abstract

Complex manufacturing processes, such as lithography, frequently depend on the coordinated operation of multiple precision motion systems whose accuracy is typically undermined by three principal limitations: dynamic coupling among the subsystems, input delays within their control loops and external disturbances present in the control input. The latter, in particular, degrades phase margin and can destabilize high-bandwidth loops. To address these challenges simultaneously, this paper introduces a novel Incremental Model Predictive Control (IMPC) framework, which integrates three key components: (i) a discrete-time state-space model that explicitly represents the coupled dynamics and the input delay, (ii) a robust positively invariant (RPI) set observer for robust state estimation, and (iii) an embedded delay compensation mechanism. Numerical simulations demonstrate that this approach achieves superior trajectory tracking performance and enhanced disturbance rejection compared to conventional approaches, thus effectively mitigating the detrimental effects of dynamic coupling and input delay.

Cybernetics & Systems
Harbin Institute of Technology (CN), Beijing Institute of Optoelectronic Technology (CN), Ministry of Industry and Information Technology (CN)
National Natural Science Foundation of China, Guangdong Key Laboratory of Precision Equipment and Manufacturing Technology, National Natural Science Foundation of China-Shenzhen Robotics Research Center Project, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 15%
Iterative Learning Control Systems
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