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Speed Synchronization of Multi-Conveyor System Using Bidirectional Interaction Topologies
Corresponding Author(s) : Agung Prayitno
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 1, February 2026
Abstract
Long production lines composed of multiple stand-alone mode controllers often face challenges when speed synchronization is required, as each setpoint must be manually adjusted one by one. This study proposes a leader-follower multi-conveyor system using distributed cooperative control, enabling all follower conveyors to synchronize their speeds with that of a designated leader. In this setup, the leader is equipped with the ability to command all followers to align their speeds to its own, which is governed by a fuzzy logic controller (FLC). Each follower operates in one of two modes: a stand-alone FLC mode or a synchronization mode using cooperative control. The cooperative control mechanism relies on speed information shared among neighboring conveyors, as defined by the system topology. Two types of bidirectional interaction topologies are explored in this work: The Bidirectional Coordinated Conveyor Topology (BCCT) and the Bidirectional Leader Coordinated Conveyor Topology (BLCCT). The proposed control scheme was implemented on a miniature multi-conveyor system, yielding RMSE values of 30.88 RPM for BCCT and 43.87 RPM for BLCCT.
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- L. He, T. Li, and B. He, “Intelligent Manufacturing Production Line Simulation of Super Capacitor,” Journal of Robotics and Control (JRC), vol. 2, no. 3, 2021, https://doi.org/10.18196/jrc.2374.
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- D. He, X. Liu, and B. Zhong, “Sustainable belt conveyor operation by active speed control,” Measurement, vol. 154, p. 107458, Mar. 2020, https://doi.org/10.1016/j.measurement.2019.107458.
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References
L. He, T. Li, and B. He, “Intelligent Manufacturing Production Line Simulation of Super Capacitor,” Journal of Robotics and Control (JRC), vol. 2, no. 3, 2021, https://doi.org/10.18196/jrc.2374.
F. Jarrahi and W. Abdul-Kader, “Performance evaluation of a multi-product production line: An approximation method,” Appl Math Model, vol. 39, no. 13, pp. 3619–3636, Jul. 2015, https://doi.org/10.1016/j.apm.2014.11.059.
F. L. Lewis, H. Zhang, K. Hengster-Movric, and A. Das, Cooperative Control of Multi-Agent Systems. London: Springer London, 2014. https://doi.org/10.1007/978-1-4471-5574-4.
Y. Wang, Z. Kou, and L. Wu, “Optimization of PID Control Parameters for Belt Conveyor Tension Based on Improved Seeker Optimization Algorithm,” Electronics (Basel), vol. 13, no. 19, p. 3907, Oct. 2024, https://doi.org/10.3390/electronics13193907.
N. N. Ab Rahman, N. Mat Yahya, and N. A. Zainal Abidin, “Investigation of the Performance of PID Control Scheme for Linear Conveyor System of Manufacturing Shopfloor,” MEKATRONIKA, vol. 4, no. 1, pp. 1–7, Mar. 2022, https://doi.org/10.15282/mekatronika.v4i1.7042.
B. D. Ushofa, L. Anifah, I. G. P. A. Buditjahjanto, and Endryansyah, “Sistem Kendali Kecepatan Putaran Motor DC pada Conveyor dengan Metode Kontrol PID,” Jurnal Teknik Elektro, vol. 11, no. 2, pp. 332–242, 2022.
S. Tiwari, A. Bhatt, A. C. Unni, J. G. Singh, and W. Ongsakul, “Control of DC Motor Using Genetic Algorithm Based PID Controller,” in 2018 International Conference and Utility Exhibition on Green Energy for Sustainable Development (ICUE), IEEE, Oct. 2018, pp. 1–6. https://doi.org/10.23919/ICUE-GESD.2018.8635662.
E. Widya Suseno and A. Ma’arif, “Tuning of PID Controller Parameters with Genetic Algorithm Method on DC Motor,” International Journal of Robotics and Control Systems, vol. 1, no. 1, pp. 41–53, Feb. 2021, https://doi.org/10.31763/ijrcs.v1i1.249.
O. M. Mirzoev and I. Z. Serdarova, “Belt Conveyor Control System Using Fuzzy Logic Algorithms Based On the Siemens S7 Controllers,” Int J Eng Res Appl, vol. 11, no. 5, pp. 10–13, 2021.
L. B. Ristic et al., “Fuzzy speed control of belt conveyor system to improve energy efficiency,” in 2012 15th International Power Electronics and Motion Control Conference (EPE/PEMC), IEEE, Sep. 2012, p. DS2a.9-1-DS2a.9-7. https://doi.org/10.1109/EPEPEMC.2012.6397260.
M. H. M. Shah, M. F. Rahmat, K. A. Danapalasingam, and N. A. Wahab, “PLC Based Adaptive Fuzzy PID Speed Control of DC Belt Conveyor System,” International Journal on Smart Sensing and Intelligent Systems, vol. 6, no. 3, pp. 1133–1152, Jan. 2013, https://doi.org/10.21307/ijssis-2017-583.
H. Wibawa, O. Wahyunggoro, and A. I. Cahyadi, “DC Motor Speed Control Using Hybrid PID-Fuzzy with ITAE Polynomial Initiation,” IJITEE (International Journal of Information Technology and Electrical Engineering), vol. 3, no. 1, p. 7, Sep. 2019, https://doi.org/10.22146/ijitee.46590.
R. Abdillah, “SISTEM KENDALI KECEPATAN KONVEYOR DENGAN BEBAN BERUBAH BERBASIS HIBRID FUZZY LOGIC-PID,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 3, Jul. 2023, https://doi.org/10.23960/jitet.v11i3.3346.
P. Li and R. Wang, “Predictive Control of Belt Conveyor Based on Recursive Fuzzy Neural Network,” in 2024 5th International Conference on Computer Engineering and Application (ICCEA), IEEE, Apr. 2024, pp. 1550–1554. https://doi.org/10.1109/ICCEA62105.2024.10603632.
W. Chen and X. Li, “Model predictive control based on reduced order models applied to belt conveyor system,” ISA Trans, vol. 65, pp. 350–360, Nov. 2016, https://doi.org/10.1016/j.isatra.2016.09.007.
Y. Kozhubaev, D. Novak, V. Karpukhin, R. Ershov, and H. Cheng, “Research on Monitoring and Control Systems for Belt Conveyor Electric Drives,” Automation, vol. 6, no. 3, p. 34, Jul. 2025, https://doi.org/10.3390/automation6030034.
Y. Erdani, G. G. Maulana, and A. Farhan, “Rancang Bangun IoT Based Monitoring System pada Multi Conveyor Untuk Perpindahan Benda,” Indonesian Journal of Computer Science, vol. 13, no. 3, Jun. 2024, https://doi.org/10.33022/ijcs.v13i3.4098.
M. Yaqot, B. C. Menezes, and J. D. Kelly, “Real-time coordination of multiple shuttle-conveyor-belts for inventory control of multi-quality stockpiles,” Comput Chem Eng, vol. 178, p. 108388, Oct. 2023, https://doi.org/10.1016/j.compchemeng.2023.108388.
T. Hu, Y. Tan, and R. Li, “Research on cooperative control strategy of multi-motor system based on fuzzy inference,” J Phys Conf Ser, vol. 2187, no. 1, p. 012002, Feb. 2022, https://doi.org/10.1088/1742-6596/2187/1/012002.
Q. Zhou, K. Shi, K. Xu, G. Du, and K. Gao, “Optimized Multi-Motor Power Control Strategy for Distributed Permanent Magnet Direct Drive Belt Conveyors,” Applied Sciences, vol. 14, no. 18, p. 8343, Sep. 2024, https://doi.org/10.3390/app14188343.
D. He, X. Liu, and B. Zhong, “Sustainable belt conveyor operation by active speed control,” Measurement, vol. 154, p. 107458, Mar. 2020, https://doi.org/10.1016/j.measurement.2019.107458.
M. Yan, J. Song, P. Yang, and L. Zuo, “Neural Adaptive Sliding‐Mode Control of a Bidirectional Vehicle Platoon with Velocity Constraints and Input Saturation,” Complexity, vol. 2018, no. 1, Jan. 2018, https://doi.org/10.1155/2018/1696851.
J. Feng, Z. Gao, and B. Guo, “State-Feedback and Nonsmooth Controller Design for Truck Platoon Subject to Uncertainties and Disturbances,” World Electric Vehicle Journal, vol. 15, no. 6, p. 251, Jun. 2024, https://doi.org/10.3390/wevj15060251.
J. Guanetti, Y. Kim, and F. Borrelli, “Control of connected and automated vehicles: State of the art and future challenges,” Annu Rev Control, vol. 45, pp. 18–40, 2018, https://doi.org/10.1016/j.arcontrol.2018.04.011.
Z. Peng, D. Wang, H. Zhang, G. Sun, and H. Wang, “Distributed model reference adaptive control for cooperative tracking of uncertain dynamical multi‐agent systems,” IET Control Theory & Applications, vol. 7, no. 8, pp. 1079–1087, May 2013, https://doi.org/10.1049/iet-cta.2012.0765.