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A Monte Carlo Dual-RLS Scheme for Improving Torque Sensing without a Sensor of a Disturbance Observer for a CMG

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  • Control Theory and Applications
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Abstract

This article introduces a novel identification technique for the inverse model of a dynamical system by a dual-recursive least square (DRLS) algorithm to estimate the disturbance torque in the disturbance observer (DOB) configuration. Since the DOB uses the inverse model to estimate the disturbance, the accurate estimation of the inverse model affects the external torque estimation performance. To estimate the inverse model more accurately, Monte Carlo simulation is conducted for two RLS filters formed a back-to-back cascaded structure to identify both forward and inverse models simultaneously. Although the inverse model can be numerically driven by the identified forward model in the conventional way, we can have the improved accuracy of estimating the inverse model by dual-RLS filters. The effects on the external torque estimation accuracy by the proposed method are experimentally verified by evaluating the performance of estimating the disturbance torque in the control moment gyroscope (CMG) actuator.

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Authors and Affiliations

  1. Intelligent Systems and Emotional Engineering laboratory, Department of Mechatronics Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon, 34134, Korea

    Sang Deok Lee & Seul Jung

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  1. Sang Deok Lee
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  2. Seul Jung
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Correspondence to Sang Deok Lee.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Pilwon Hur under the direction of Editor Won-jong Kim. This paper was supported by the National Research Foundation of Korea in 2017.

Sang-Deok Lee received his B.S. and M.S. degrees in Electronics Engineering from Cheonbuk National University, in 1998 and 2003, respectively. He joined LG Precision and Samsung Heavy Industry from 1998 to 2000 and from 2003 to 2014, respectively. He received his Ph.D. degree at Department of Mechatronics Engineering at Chungnam National University in 2018. His research interests are Mechatronic system identification and control.

Seul Jung received his B.S. degree in Electrical and Computer Engineering from Wayne State University, Detroit, MI, USA in 1988, and his M.S. and Ph.D. degrees in Electrical and Computer Engineering from the University of California, Davis, in 1991 and 1996, respectively. In 1997, he joined the Department of Mechatronics Engineering, Chungnam National University, where he is presently a professor. His research interests include intelligent Mechatronics systems, intelligent robotic systems, autonomous navigation, gyroscope applications, and robot education.

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Lee, S.D., Jung, S. A Monte Carlo Dual-RLS Scheme for Improving Torque Sensing without a Sensor of a Disturbance Observer for a CMG. Int. J. Control Autom. Syst. 18, 1530–1538 (2020). https://doi.org/10.1007/s12555-018-0416-z

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  • DOI: https://doi.org/10.1007/s12555-018-0416-z

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