Neuro-fuzzy control of bilateral teleoperation system with transmission time delay and force feedback using Arduino Due board

Majlesi Journal of Electrical Engineering

مقاله 14، دوره 19، شماره 2، شهریور 2025، صفحه 1-14   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2025.710229

نویسندگان

Hakima Rahem^{* 1}؛ Rabah Mellah¹؛ Massinissa Zidane¹؛ Abdelhakim Saim²

¹Department of Automatics, Faculty of Electrical and Computer Engineering, University Mouloud Mammeri of Tizi-Ouzou, Tizi-Ouzou, Algeria.

²IREENA Laboratory, Department of Electrical Engineering, University of Nantes, Saint-Nazaire, France.

چکیده

In this paper, an intelligent adaptive controller is designed to handle the transmission time delay problem in a complex and nonlinear bilateral teleoperation system with force feedback. The combination of the inference capacities of fuzzy logic reasoning and the learning capacities of neural networks, enable the formation of an adaptive and robust neuro-fuzzy controller ANFIS (Adaptive Neuro-Fuzzy Inference System) which adapts to variations in the dynamics of the system by the automatic adjustment of the parameters of the fuzzy rules and the membership functions using a learning algorithm, thus ensuring compensation of the negative effects of transmission delay in the closed loop system. Thanks to the speed and power of the microprocessor of the Arduino Due board and the functionalities of Matlab-Simulink, a real experimental platform of master-slave teleoperation system with transmission delay and force feedback is designed. In order to demonstrate the efficiency, adaptability and advantages of ANFIS controller, several comparative experiments are carried out using different controllers (ANFIS, conventional PI and PI regulator with the modified wave variable method PI+MWVM). The experimental results clearly show the high system performance achieved with the ANFIS
controller.

کلیدواژه‌ها

ANFIS؛ Arduino Due؛ Bilateral teleoperation؛ Stability؛ Time delay؛ Wave variables

مراجع

1. H. Tabatabaei, A. H. Zaeri, and M. Vahedi, “Stable linear bilateral teleoperation system employing an impedance control via estimated external forces,” Majlesi Journal of Electrical Engineering, vol. 14, no. 2, pp. 35-42, 2020.
2. A. Manocha, N. Pernalete, And R.V. Dubey, “Variable position mapping based assistance in teleoperation for nuclear cleanup,” in proceedings ICRA. International Conference in Robotics and Automation IEEE, pp. 374-379, 2001. DOI:10.1109/ROBOT.2001.932580.
3. Artigas, and G. Hirzinger, “A brief history of DLR's space telerobotics and force feedback teleoperation,” Acta Polytechnica Hungarica, vol. 13, pp. 239-249, 2016. DOI: 10.12700/APH.13.1.2016.1.16.
4. Arbeille, K. Zuj, A. Saccomandi, E. Andre, C. De La Porte, and M. Georgescu, “Tele-operated echography and remote guidance for performing tele-echography on geographically isolated patients,” journal of Clinical Medicine, vol. 5, pp. 1-9, 2016. DOI: 10.3390/jcm5060058.
5. Alfi, and M. Farrokhi, “A simple structure for bilateral transparent teleoperation systems with time delay,” Journal of Dynamic Systems, Measurement and Control. vol. 37. pp. 1-9, 2008. DOI: 10.1115/1.2936854.
6. Rahem, R. Mellah and M. Zidane, “implementation of the neuro-fuzzy controller for delayed position-position teleoperation system using Arduino due board,” 28^th international conference on methods and models in automation and robotic (MMAR), Aug 27-30, Miedzyzdroje, Poland, pp.181-186, 2024. DOI:10.1109/MMAR62187.2024.10680763.
7. Guo, C. Liu, and P. Poignet, “A scaled bilateral teleoperation system for robotic-assisted surgery with time delay,” Journal of Intelligent & Robotic Systems Springer Verlag. vol. 95. pp. 165-192, 2019. DOI: 10.1007/s10846-018-0918-1
8. Chen, F. Hung, W. Sun, and W. Song, “An improved wave-variable based four-channel control design in bilateral teleoperation system for time-delay compensation,” IEEE Access, vol.6. pp. 12848-12857, 2018. DOI: 10.1109/ACCESS.2018.2805782.
9. A. Lawrence, “Stability and transparency in bilateral teleoperation,” IEEE Transactions on robotics and automation, vol. 9. pp. 624-637,1993. DOI: 10.1109/70.258054.
10. Niemeyer, and J-J. E. Slotine, “Stable adaptive teleoperation,” IEEE Journal of oceanic engineering, vol. 16, pp. 152-162, 1991. DOI: 10.1109/48.64895.
11. Bate, C.D. Cook, and Z. Li, “Reducing wave-based teleoperator reflections for unknown environments,” IEEE Transactions on Industrial Electronics, vol 58, pp. 392-397, 2011. DOI: 10.1109/TIE.2009.2035994.
12. Chen, F. Huang, C. Yang, and Y. Bin, “Adaptive fuzzy backstepping control for stable nonlinear bilateral teleoperation manipulators with enhanced transparency performance,” IEEE Transactions on industrial electronics, vol. 67, pp. 746-756, 2020. DOI: 10.1109/TIE.2019.2898587.
13. J. Pan, C. Canudas-de-Wit, and O. Sename, “A new predictive approach for bilateral teleoperation with applications to drive-by-wire systems,” IEEE Transactions on Robotics, vol. 22, pp. 1146-1162, 2006. DOI: 10.1109/TRO.2006.886279.
14. C. Cho, and J. H. Park, “Stable bilateral teleoperation under a time delay using a robust impedance control,” ELSEVIER, Mechatronics, vol. 15, pp. 611-625, 2005. DOI: 10.1016/j.mechatronics.2004.05.006.
15. Erfani, S. Rezaei, M. Pourseifi, and H.E. Derili, “Optimal control in teleoperation systems with time delay: a singular perturbation approach,” Journal of Computational and Applied Mathematics, vol. 338, pp. 168-184, 2018. DOI: 10.1016/j.cam.2018.01.026.
16. Fattouh, and O. Sename, “Finite spectrum assignment for teleoperation systems with time delay,” in proceedings of the IEEE Conference on Control and Decision, Hawaii, USA. 2003. DOI: 10.1109/CDC.2003.1272215.
17. Tabatabaee and S. M. Sayed Mosavi, “Robust H-infinity Takagi-Sugeno fuzzy controller design for a bilateral tele-operation system via LMIs,” Majlesi Journal of Electrical Engineering, vol. 5, no. 2, pp.1-9, 2011.
18. M. Htun, A. N. Yakunin, H. S. Paing, and K. Win, “Implementation of ANFIS controller for DC motor on an Arduino Due Board,” IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering., pp. 2668-2671,2021. DOI: 10.1109/ElConRus51938.2021.9396740.
19. Wang, C. Sabourin, and K. Madani, “ANFIS controller for non-holonomic robots,” Majlesi Journal of Electrical Engineering, vol. 5, no. 2, pp.31-37, 2011.
20. Mellah, S. Guermah, and R. Toumi, “Adaptive control of Bilateral Teleoperation System with compensatory neural-fuzzy controllers,” International Journal of Control, Automation and Systems, vol. 15, pp. 1949-1959, 2017. DOI: 10.1007/s12555-015-0309-3.
21. K. Pouya, “Design of Adaptive Neural Fuzzy Controller for Speed Control of BLDC Motors,” Majlesi Journal of Electrical Engineering, vol. 11, no. 1, pp. 37-43, 2017.
22. O-S. Vargas, S. Aldaco, J.A Alquicira, LGV Valdés, and Núñez, “Adaptive Network-Based Fuzzy Inference System (ANFIS) Applied to Inverters: A Survey”, IEEE Transactions on Power Electronics, vol. 39, pp. 869 – 884, 2024. DOI: 10.1109/TPEL.2023.3327014.
23. Po-Ngaen, “Adaptive four-channel neuro-fuzzy control of a master-slave robot,” International Journal of Advanced Robotic Systems, vol. 10, pp. 1-8, 2013. DOI: 10.5772/55591.
24. Chen, F. Huang, W. Sun, J. Gu, and B. Yao, “RBF- neural network-based adaptive robust control for nonlinear bilateral teleoperation manipulators with uncertainty and time delay,” IEEE/ASME Transactions in Mechatronics, vol. 25, pp. 906-918, 2020. DOI: 10.1109/TMECH.2019.2962081.
25. Abut, and S. Soyguder, “Controller design and application for single-dof teleoperation system under uncertain dynamics and time delay problem,” Euroasia Summit, 2nd international applied science congress., pp. 16-23, 2021.
26. Zhang, A. Song, H. Li, and S. Shaobo, “Novel adaptive finite-time control of teleoperation system with time-varying delays and input saturation,” IEEE Transactions on cybernetics, vol.51, pp. 3724-3737, 2021. DOI: 10.1109/TCYB.2019.2924446.
27. Zhang, S. Yuan, X. Yu, L., Kong, Q. Li, and G. Li, “Adaptive neural network fixed-time control design for bilateral teleoperation with time delay,” IEEE Transactions on cybernetics, vol. 52, pp. 9756-9769, 2022. DOI: 10.1109/TCYB.2021.3063729.
28. Sun, F. Naghdy, and H. Du, “Neural network-based passivity control of teleoperation system under time-varying delays,” IEEE Transactions on Cybernetics, vol. 47, pp. 1666-1680, 2017. DOI: 10.1109/TCYB.2016.2554630.
29. Sun, Q. Liao, and H. Ren, “Type-2 fuzzy modeling and control for bilateral teleoepration system with dynamic uncertianties and time-varying delays,” IEEE transactions on industrial electronics, vol. 65, pp. 447-459, 2018. DOI: 10.1109/TIE.2017.2719604.
30. R Ferrell, “Remote manipulation with transmission delay,” IEEE Trans. On Human Factors in Electronics., vol. 6, pp. 24-32, 1965. DOI: 10.1109/THFE.1965.6591253.
31. Mellah, R. Toumi, “Control bilateral teleoperation by compensatory ANFIS,” Advanced Mechatronics Solutions, vol. 393, pp. 167-172, 2016. DOI: 10.1007/978-3-319-23923-1_25.
32. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 665-685, 1993. DOI: 10.1109/21.256541.
33. Mellah, R. Toumi, “Compensatory neuro-fuzzy control of bilateral teleoperation system,” 20^th International Conference on Methods and Models in Automation and Robotics(MMRA), Miedzyzdroje, Poland, pp.382-387, 2015. DOI: 10.1109/MMAR.2015.7283906.
34. Ruder, “An overview of gradient descent optimization algorithms,” Insight Center for Data Analytics, NUI Galway Aylien Ltd., Dublin., pp. 1-14, 2017. DOI: 10.48550/arXiv.1609.04747.
35. Haykin, “Kalman filtering and neural networks”, John Wiley & Sons, Inc.,2001. DOI:10.1002/0471221546.
36. Melin, C. Baiere, E. Espinosa, J. Riedemann, J. Espinosa, and R. Pena, “Study of the open-source Arduino Due Board as digital control platform for three-phase power converters,” IEEE ACCESS, vol. 10, pp. 7574-7584, 2022. DOI: 10.1109/ACCESS.2021.3138705.
37. Ome, and G. Rao Someswara, “Internet of Things (IoT) based sensors to cloud system using ESP8266 and Arduino Due,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, pp. 336-343, 2016. DOI: 10.17148/IJARCCE.2016.51069.
38. Bartmann, “Le grand livre d’Arduino,” 2eme Edition, Eyrolles Editions, Paris Cedex 05.,2015, (In French).
39. Peng, and P. Y. Woo, “Neural-fuzzy control system for robotic manipulators,” IEEE Control Systems Magazing, pp. 53-63, 2002. DOI: 10.1109/37.980247.
40. Forouzantabar, H. Talebi and A.k. Sedigh, “Adaptive neural network control of bilateral teleoperation with time delay,” 2nd International Conference on Control, Instrumentation and Automation, pp. 871-876, 2011. DOI: 10.1007/s11071-011-0057-8.
41. M. Kebria, A. Khosravi, and S. Nahavandi, “Neural Network Control of teleoperation systems with delay and uncertainties based on multilayer perceptron estimations,” International Joint Conference on Neural Networks(IJCNN), pp. 1-7, 2020. DOI: 10.1109/IJCNN48605.2020.9207035.
42. Uyulan “Robust passivity-based nonlinear controller design for bilateral teleoperation system under variable time delay and variable load disturbance”. Nonlinear Eng.vol. 13, pp. 1-29, 2024. DOI: 10.1515/nleng-2022-0358.
43. S. Sarkhooni, B. Yazdankhoo. M. R. Hairi Yazdi, and F. Najafi, “Fuzzy logic-based variable impedance control for a bilateral teleoperation system under time delay,” Journal of Computational Applied Mechanics, vol. 55, pp. 500-518, 2024.  DOI: 10.22059/JCAMECH.2024.369060.914.
44. F. D. Rosli, W. M. Utomo, A. Abubakar, S. Salimin, T. Sithananthan, “Optimization of ANFIS-PID performance in bidirectional buck-boost DC-DC converter,” Majlesi Journal of Electrical Engineering (MJEE), vol. 18, no. 2, pp. 1-10, 2024. DOI: 10.57647/j.mjee.2024.1802.40.
45. Kumar, J Ohri, “Haptic interface controller using intelligent techniques,” Majlesi Journal of Electrical Engineering, vol. 14, no. 4, pp.67-74. 2020. DOI: 10.29252/mjee.14.4.67.