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Optimization of DC Motor Control System FL57BL02 Using Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT): Performance Analysis
1
Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung 402, Taiwan
2
Department of Marine Electrical Engineering, Shipbuilding Institute of Polytechnic Surabaya, Surabaya 60111, Indonesia
* Corresponding Author:
Anggara Trisna Nugraha,
[email protected]
Volume 1, Issue 1
2025
Received: 29 March 2025
Accepted: 22 May 2025
Published: 28 July 2025
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Article Information
Volume/Issue
Volume 1, Issue 1, 2025
Pages
15-22
Abstract
Optimization is a critical approach in decision-making processes aimed at enhancing both safety and operational efficiency in industrial systems. This research focuses on the implementation of the Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) control strategies to optimize the FL57BL02 DC motor control system, widely used in industrial automation, particularly in high-risk applications such as conveyors and robotic systems. The LQR method is employed to enhance system stability by minimizing output deviations and ensuring optimal control performance under varying load conditions. Meanwhile, LQT is utilized to improve trajectory tracking accuracy, ensuring that the system follows the desired reference with minimal error. Through comprehensive simulation and experimental validation, this study demonstrates that the integration of LQR and LQT control strategies reduces output deviation and transient response errors by up to 25% compared to conventional PID-based controllers. Furthermore, the implementation of these advanced control techniques contributes significantly to Occupational Safety and Health (OHS) compliance by mitigating mechanical vibrations and reducing noise levels both of which are crucial risk factors in industrial environments. By stabilizing system performance, this research presents a novel engineering solution that enhances machine reliability, minimizes downtime, and mitigates the potential for workplace hazards. This study offers an important contribution to the field of automatic control systems by demonstrating how advanced optimal control strategies can be leveraged to enhance industrial safety, improve energy efficiency, and pave the way for the development of more sophisticated OHS-compliant automation technologies in future industrial applications.
Graphical Abstract
Keywords
DC motor control
linear quadratic regulator (LQR)
linear quadratic tracking (LQT)
optimization
engineering application
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
References
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APA Style
Rohman, Y. F., Nugraha, A. T., & Sobhita, R. A. (2025). Optimization of DC Motor Control System FL57BL02 Using Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT): Performance Analysis. ICCK Transactions on Power Electronics and Industrial Systems, 1(1), 15–22. https://doi.org/10.62762/TPEIS.2025.356246
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TY - JOUR AU - Rohman, Yulian Fatkur AU - Nugraha, Anggara Trisna AU - Sobhita, Rama Arya PY - 2025 DA - 2025/07/28 TI - Optimization of DC Motor Control System FL57BL02 Using Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT): Performance Analysis JO - ICCK Transactions on Power Electronics and Industrial Systems T2 - ICCK Transactions on Power Electronics and Industrial Systems JF - ICCK Transactions on Power Electronics and Industrial Systems VL - 1 IS - 1 SP - 15 EP - 22 DO - 10.62762/TPEIS.2025.356246 UR - https://www.icck.org/article/abs/TPEIS.2025.356246 KW - DC motor control KW - linear quadratic regulator (LQR) KW - linear quadratic tracking (LQT) KW - optimization KW - engineering application AB - Optimization is a critical approach in decision-making processes aimed at enhancing both safety and operational efficiency in industrial systems. This research focuses on the implementation of the Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) control strategies to optimize the FL57BL02 DC motor control system, widely used in industrial automation, particularly in high-risk applications such as conveyors and robotic systems. The LQR method is employed to enhance system stability by minimizing output deviations and ensuring optimal control performance under varying load conditions. Meanwhile, LQT is utilized to improve trajectory tracking accuracy, ensuring that the system follows the desired reference with minimal error. Through comprehensive simulation and experimental validation, this study demonstrates that the integration of LQR and LQT control strategies reduces output deviation and transient response errors by up to 25% compared to conventional PID-based controllers. Furthermore, the implementation of these advanced control techniques contributes significantly to Occupational Safety and Health (OHS) compliance by mitigating mechanical vibrations and reducing noise levels both of which are crucial risk factors in industrial environments. By stabilizing system performance, this research presents a novel engineering solution that enhances machine reliability, minimizes downtime, and mitigates the potential for workplace hazards. This study offers an important contribution to the field of automatic control systems by demonstrating how advanced optimal control strategies can be leveraged to enhance industrial safety, improve energy efficiency, and pave the way for the development of more sophisticated OHS-compliant automation technologies in future industrial applications. SN - request pending PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Rohman2025Optimizati,
author = {Yulian Fatkur Rohman and Anggara Trisna Nugraha and Rama Arya Sobhita},
title = {Optimization of DC Motor Control System FL57BL02 Using Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT): Performance Analysis},
journal = {ICCK Transactions on Power Electronics and Industrial Systems},
year = {2025},
volume = {1},
number = {1},
pages = {15-22},
doi = {10.62762/TPEIS.2025.356246},
url = {https://www.icck.org/article/abs/TPEIS.2025.356246},
abstract = {Optimization is a critical approach in decision-making processes aimed at enhancing both safety and operational efficiency in industrial systems. This research focuses on the implementation of the Linear Quadratic Regulator (LQR) and Linear Quadratic Tracking (LQT) control strategies to optimize the FL57BL02 DC motor control system, widely used in industrial automation, particularly in high-risk applications such as conveyors and robotic systems. The LQR method is employed to enhance system stability by minimizing output deviations and ensuring optimal control performance under varying load conditions. Meanwhile, LQT is utilized to improve trajectory tracking accuracy, ensuring that the system follows the desired reference with minimal error. Through comprehensive simulation and experimental validation, this study demonstrates that the integration of LQR and LQT control strategies reduces output deviation and transient response errors by up to 25\% compared to conventional PID-based controllers. Furthermore, the implementation of these advanced control techniques contributes significantly to Occupational Safety and Health (OHS) compliance by mitigating mechanical vibrations and reducing noise levels both of which are crucial risk factors in industrial environments. By stabilizing system performance, this research presents a novel engineering solution that enhances machine reliability, minimizes downtime, and mitigates the potential for workplace hazards. This study offers an important contribution to the field of automatic control systems by demonstrating how advanced optimal control strategies can be leveraged to enhance industrial safety, improve energy efficiency, and pave the way for the development of more sophisticated OHS-compliant automation technologies in future industrial applications.},
keywords = {DC motor control, linear quadratic regulator (LQR), linear quadratic tracking (LQT), optimization, engineering application},
issn = {request pending},
publisher = {Institute of Central Computation and Knowledge}
}
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