Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions

Kazezkhan, Guljaina; Wang, Na; Xu, Qian; Lin, Shangmin; Wang, Hui; Xue, Fei; He, Feilong; Cao, Xiaoman

doi:10.3390/machines13060484

Open AccessArticle

Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions

by

Guljaina Kazezkhan

^1,2

,

Na Wang

^1,2,3,*,

Qian Xu

^1,2,3,*,

Shangmin Lin

⁴

,

Hui Wang

¹

,

Fei Xue

¹,

Feilong He

¹ and

Xiaoman Cao

¹

Xinjiang Astronomical Observatory, Chinese Academy of Sciences, Urumqi 830011, China

²

Key Laboratory of Radio Astronomy and Technology, Beijing 100101, China

³

Xinjiang Key Laboratory of Radio Astrophysics, Urumqi 830011, China

⁴

Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

^*

Authors to whom correspondence should be addressed.

Machines 2025, 13(6), 484; https://doi.org/10.3390/machines13060484

Submission received: 25 April 2025 / Revised: 23 May 2025 / Accepted: 1 June 2025 / Published: 3 June 2025

(This article belongs to the Section Machine Design and Theory)

Download

Browse Figures

Review Reports Versions Notes

Abstract

Accurate kinematic calibration of subreflector actuators is essential for pointing precision of large radio telescopes, particularly at high frequencies. Conventional least-squares methods are vulnerable to noise and outliers, and their accuracy may degrade when limited pose diversity leads to poor parameter excitation. To address these challenges, this paper proposes a novel robust self-calibration framework that integrates Huber loss and

L_{2}

regularization into the Levenberg–Marquardt (LM) algorithm—yielding a hybrid optimization approach that combines residual robustness, numerical stability, and convergence reliability. A comprehensive simulation study was conducted under varying workspace sizes and sensor noise levels. The proposed method maintained stable performance even under reduced excitation and high-noise conditions, where traditional LM methods typically degrade, confirming its robustness and applicability to realistic calibration scenarios. The framework was further validated using a structured-light 6-DOF pose measurement system, the proposed method achieved over 90% improvement in both position and orientation accuracy compared to the traditional LM approach. These findings confirm the method’s effectiveness for high-precision 6-DOF calibration in parallel mechanisms, and its suitability for real-world applications in radio telescope subreflector alignment.

Keywords: radio telescope; subreflector actuator; kinematic calibration; Levenberg–Marquardt algorithm; Huber loss; L₂ regularization; parallel platform

Share and Cite

MDPI and ACS Style

Kazezkhan, G.; Wang, N.; Xu, Q.; Lin, S.; Wang, H.; Xue, F.; He, F.; Cao, X. Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions. Machines 2025, 13, 484. https://doi.org/10.3390/machines13060484

AMA Style

Kazezkhan G, Wang N, Xu Q, Lin S, Wang H, Xue F, He F, Cao X. Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions. Machines. 2025; 13(6):484. https://doi.org/10.3390/machines13060484

Chicago/Turabian Style

Kazezkhan, Guljaina, Na Wang, Qian Xu, Shangmin Lin, Hui Wang, Fei Xue, Feilong He, and Xiaoman Cao. 2025. "Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions" Machines 13, no. 6: 484. https://doi.org/10.3390/machines13060484

APA Style

Kazezkhan, G., Wang, N., Xu, Q., Lin, S., Wang, H., Xue, F., He, F., & Cao, X. (2025). Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions. Machines, 13(6), 484. https://doi.org/10.3390/machines13060484

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Robust Self-Calibration of Subreflector Actuators Under Noise and Limited Workspace Conditions

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI