PD algorithm combined with Sugeno fuzzy logic improves trajectory tracking control quality for Delta parallel robot

2270 views

Authors

  • Dinh Xuan Minh Faculty of Electrical Engineering, Hanoi University of Industry
  • Pham Van Hung (Corresponding Author) Faculty of Electrical Engineering, Hanoi University of Industry
  • Nguyen Nam Khanh Faculty of Electrical Engineering, Hanoi University of Industry
  • Mai The Thang Faculty of Electrical Engineering, Hanoi University of Industry
  • Ha Minh Quan Faculty of Electrical Engineering, Hanoi University of Industry
  • Ha Viet Anh Faculty of Electrical Engineering, Hanoi University of Industry

DOI:

https://doi.org/10.54939/1859-1043.j.mst.93.2024.38-46

Keywords:

Delta 3-DOF; Model-based PD controller; Sugeno fuzzy logic; Trajectory tracking control.

Abstract

 The research aims to improve the model-based PD algorithm by using Sugeno fuzzy logic to adjust the parameter values of the controller online, improving the trajectory tracking quality and robustness to noise for the Delta parallel robot. The study uses Matlab and Simulink simulation tools to validate the reliability of the controller on an eight-shaped trajectory. The simulation results show that the control performance is very good when it is possible to control the motion of the Delta 3-DOF parallel robot follow the desired trajectory and maintain stability with a fast settling time , even when the system is affected by unknown external disturbances.

References

[1]. Nguyễn Đình Dũng, “Động lực học ngược và điều khiển chuyển động của robot song song delta không gian”, Luận án tiến sĩ kỹ thuật cơ khí và cơ kỹ thuật, Viện Hàn lâm Khoa học và Công nghệ Việt Nam, tr. 78-79, (2018).

[2]. Lê Minh Thành et al, “Chỉnh định bộ điều khiển pid bằng hệ mờ áp dụng cho robot delta ba bậc tự do”, Tạp chí Khoa học và Công nghệ Đại học Thái Nguyên (2022). DOI: https://doi.org/10.34238/tnu-jst.5290

[3]. Le Minh Thanh et al , “Evaluating the Quality of Intelligent Controllers for 3-DOF Delta Robot Control”, International Journal of Mechanical Engineering and Robotics Research (2021). DOI: https://doi.org/10.18178/ijmerr.10.10.542-552

[4]. Le Minh Thanh et al , “Delta Robot Control Using Single Neuron PID Algorithms Based on Recurrent Fuzzy Neural Network Identifiers”, International Journal of Mechanical Engineering and Robotics Research (2020). DOI: https://doi.org/10.18178/ijmerr.9.10.1411-1418

[5]. Le Minh Thanh et al , “Optimization of PID controller by genetic algorithm experiment on delta robot”, Measurement, Control, and Automation (2022).

[6]. Aguilar-Mejia et al, “Adaptive control of 3-DOF Delta parallel robot”, In 2019 IEEE International Autumn Meeting on Power, Electronics and Computing (2019). DOI: https://doi.org/10.1109/ROPEC48299.2019.9057075

[7]. Sugeno M, “Industrial applications of fuzzy control”, Elsevier Science Inc (1985).

[8]. R. Tipsuwanporn et al, “Fuzzy Logic PID controller based on FPGA for process control”, IEEE International Symposium on Industrial Electronics, vol. 2, pp. 1495-1500, (2004). DOI: https://doi.org/10.1109/ISIE.2004.1572035

[9]. Castañeda et al, “Robust trajectory tracking of a delta robot through adaptive active disturbance rejection control”, IEEE Transactions on control systems technology, 23(4), 1387-1398 (2014). DOI: https://doi.org/10.1109/TCST.2014.2367313

[10]. A. Zubizarreta et al, “Robust Model Based Predictive Control for Trajectory Tracking of Parallel Robots”, In book: New Advances in Mechanisms, Transmissions and Applications (2014). DOI: https://doi.org/10.1007/978-94-007-7485-8_42

[11]. Y. Hu et al, “Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model”, in IEEE Transactions on Neural Networks and Learning Systems (2020).

[12]. Xi Chen et al, “Industrial Robot Control with Object Recognition based on Deep Learning”, Procedia CIRP, Volume 76, Pages 149-154, ISSN 2212-8271, (2018). DOI: https://doi.org/10.1016/j.procir.2018.01.021

[13]. Tobias Gold et al, “Model Predictive Interaction Control for Industrial Robots”, IFAC-PapersOnLine, Volume 53, Issue 2, Pages 9891-9898, ISSN 2405-8963, (2020). DOI: https://doi.org/10.1016/j.ifacol.2020.12.2696

[14]. Rongrong Liu et al, “Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review”, Robotics 10(22):1-13, (2021). DOI: https://doi.org/10.3390/robotics10010022

[15]. Yen, V.T., Nan, W.Y. & Van Cuong, P, “Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators”, Neural Comput & Applic (2019). DOI: https://doi.org/10.1007/s12555-018-0210-y

[16]. Dang, Son Tung, et al. “Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks.” Electronics 12.11: 2345, (2023). DOI: https://doi.org/10.3390/electronics12112345

Downloads

Published

25-02-2024

How to Cite

Đinh Xuân Minh, H. Pham, Nguyễn Nam Khánh, Mai Thế Thắng, Hà Minh Quân, and Hà Việt Anh. “PD Algorithm Combined With Sugeno Fuzzy Logic Improves Trajectory Tracking Control Quality for Delta Parallel Robot”. Journal of Military Science and Technology, vol. 93, no. 93, Feb. 2024, pp. 38-46, doi:10.54939/1859-1043.j.mst.93.2024.38-46.

Issue

Vol. 93 (2024)

Section

Research Articles

Categories