Volume 17, Issue 1 (Journal of Control, V.17, N.1 Spring 2023)
JoC 2023, 17(1): 1-15 |
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Abooee A, Koofeh M, Allahbakhshi M. A Combined Finite-Time Control Framework for Exoskeleton Robots by Utilizing Adaptive-Robust Nonlinear Control Method. JoC 2023; 17 (1) :1-15
URL: http://joc.kntu.ac.ir/article-1-951-en.html
URL: http://joc.kntu.ac.ir/article-1-951-en.html
1- Department of Electrical Engineering, Yazd University, Yazd, Iran
2- Yazd University
3- Shiraz University
2- Yazd University
3- Shiraz University
Abstract: (20002 Views)
In this study, by using the adaptive-robust nonlinear control approach, an innovative hybrid finite-time control framework is introduced to tackle the tracking problem for a great group of exoskeleton robots (in the presence of friction forces, two types of uncertainties, and unknown forces generated by the disabled person). According to the tracking aim, angular displacement of robot must exactly tend to required trajectories within the finite time. Firstly, a general nonlinear model is represented to characterize dynamical behavior of a typical exoskeleton robot possessing unknown physical constants. To complete this model, friction forces, modelling uncertainties, and unknown human torques (external disturbances) are considered. Two components of the exoskeleton model (unknown friction forces and parametric uncertainties) are rewritten as two detached linear regression forms. Secondly, a finite-time adaptive-robust nonlinear control structure is proposed to accomplish the aforementioned tracking aim and, as a result, the global finite-time stability is provided for the closed-loop exoskeleton robot. The mentioned finite-time nonlinear controllers are designed by combining the adaptation rules and the terminal sliding mode control strategy (along with new defined sliding manifolds). These adaptation rules estimates model physical constants, unknown coefficients of the friction forces, unknown human torques and upper bound of the Euclidean norm of the external disturbance vector. Mathematical analysis illustrates that time responses of the estimations precisely tend to constant values after the finite-time. Eventually, the combined finite-time control framework is simulated onto the 2-DOF exoskeleton numerically and obtained results reveal that the proposed control structure appropriately provides the finite-time tracking objective. the maximum power point tracking objective.
Keywords: Global finite-time stability, Exoskeleton robot, Parametric uncertainty and unknown friction, Terminal sliding mode control, Finite-time adaptation rules
Type of Article: Research paper |
Subject:
Special
Received: 2022/09/6 | Accepted: 2023/06/6 | ePublished ahead of print: 2023/06/10 | Published: 2023/06/22
Received: 2022/09/6 | Accepted: 2023/06/6 | ePublished ahead of print: 2023/06/10 | Published: 2023/06/22
References
1. [1] P. Yang, X. Ma, J. Wang, G. Zhang, Y. Zhang, and L. Chen, "Disturbance observer based terminal sliding mode control of a 5-DOF upper-limb exoskeleton robot," IEEE Access, vol. 7, no. 1, pp. 62833-62839, 2019. [DOI:10.1109/ACCESS.2019.2911348]
2. [2] M. Sharifi, J. K. Mehr, V. K. Mushahwar, and M. Tavakoli, "Autonomous locomotion trajectory shaping and nonlinear control for lower limb exoskeletons," IEEE/ASME Transactions on Mechatronics, vol. 27, no. 2, pp. 645-655, 2022. [DOI:10.1109/TMECH.2022.3156168]
3. [3] G. Zhang, J. Wang, P. Yang, and S. Guo, "A learning control scheme for upper-limb exoskeleton via adaptive sliding mode technique," Mechatronics, vol. 86, no. 1, pp. 102832 (1-12), 2022. [DOI:10.1016/j.mechatronics.2022.102832]
4. [4] M. Khamar, M. Edrisi, and S. Forghany, "Designing a robust controller for a lower limb exoskeleton to treat an individual with crouch gait pattern in the presence of actuator saturation," ISA Transactions, vol. 126, no. 1, pp. 513-532, 2022. [DOI:10.1016/j.isatra.2021.08.027]
5. [5] Y. Yang, Y. Li, X. Liu, and D. Huang, "Adaptive neural network control for a hydraulic knee exoskeleton with valve dead-band and output constraint based on nonlinear disturbance observer," Neurocomputing, vol. 473, no. 7, pp.14-23, 2022. [DOI:10.1016/j.neucom.2021.12.010]
6. [6] J. Han, S. Yang, L. Xia, and Y.H. Chen, "Deterministic adaptive robust control with a novel optimal gain design approach for a fuzzy 2-DOF lower limb exoskeleton robot system," IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2373-2387, 2021. [DOI:10.1109/TFUZZ.2020.2999739]
7. [7] L. Gao, L.J. Zhao, G.S. Yang, and C.J. Ma, "A digital twin-driven trajectory tracking control method of a lower-limb exoskeleton," Control Engineering Practice, vol. 127, no. 1, pp. 105271 (1-14), 2022. [DOI:10.1016/j.conengprac.2022.105271]
8. [8] V. Molazadeh, Q. Zhang, X. Bao, and N. Sharma, "An iterative learning controller for a switched cooperative allocation strategy during sit-to-stand tasks with a hybrid exoskeleton," IEEE Transactions on Control Systems Technology, vol. 30, no. 3, pp. 1021-1036, 2022. [DOI:10.1109/TCST.2021.3089885]
9. [9] L. Teng, M. A. Gull and S. Bai, "PD-based fuzzy sliding mode control of a wheelchair exoskeleton robot," IEEE/ASME Transactions on Mechatronics, vol. 25, no. 5, pp. 2546-2555, 2020. [DOI:10.1109/TMECH.2020.2983520]
10. [10] J. Zhao, T. Yang, X. Sun, J. Dong, Z. Wang, and C. Yang, "Sliding mode control combined with extended state observer for an ankle exoskeleton driven by electrical motor," Mechatronics, vol. 76, no. 1, pp. 102554 (1-12), 2021. [DOI:10.1016/j.mechatronics.2021.102554]
11. [11] A. Abooee, M. M. Arefi, F. Sedghi and V. Abootalebi, "Robust nonlinear control schemes for finite-time tracking objective of a 5-DOF robotic exoskeleton," International Journal of Control, vol. 92, no. 9, pp. 2178-2193, 2019. [DOI:10.1080/00207179.2018.1430379]
12. [12] A. Razzaghian "A fuzzy neural network-based fractional-order Lyapunov-based robust control strategy for exoskeleton robots: Application in upper-limb rehabilitation," Mathematics and Computers in Simulation, vol. 193, no. 3, pp. 567-583, 2022. [DOI:10.1016/j.matcom.2021.10.022]
13. [13] S. Bembli, N. K. Haddad, and S. Belghith, "Model free terminal sliding mode with gravity compensation control of a 2 DOF exoskeleton-upper limb system," International Journal of Mechanical and Mechatronics Engineering, vol. 14, no. 9, pp. 451-457, 2020.
14. [14] G. W. Zhang, P. Yang, J. Wang, J. J. Sun, and Y. Zhang, "Integrated observer-based fixed-time control with backstepping method for exoskeleton robot," International Journal of Automation and Computing, vol. 17, no. 1, pp. 71-82, 2019. [DOI:10.1007/s11633-019-1201-z]
15. [15] W. Sun, J.W. Lin, S.F. Su, N. Wang, and M. Joo Er, "Reduced adaptive fuzzy decoupling control for lower limb exoskeleton," IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1099-1109, 2021. [DOI:10.1109/TCYB.2020.2972582]
16. [16] J. Wang and O. R. Barry, "Inverse optimal robust adaptive controller for upper limb rehabilitation exoskeletons with inertia and load uncertainties," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2171-2178, 2021. [DOI:10.1109/LRA.2021.3061361]
17. [17] Z. Zhao, L. Hao, M. Liu, H. Gao, and X. Li, "Prescribed performance model-free adaptive terminal sliding mode control for the pneumatic artificial muscles elbow exoskeleton," Journal of Mechanical Science and Technology, vol. 35, no. 7, pp. 3138-3197, 2021. [DOI:10.1007/s12206-021-0639-4]
18. [18] R.P. San Lazaro, I. Salgado, and I. Chairez, "Adaptive sliding-mode controller of a lower limb mobile exoskeleton for active rehabilitation," ISA Transactions, vol. 109, no. 1, pp. 218-228, 2021. [DOI:10.1016/j.isatra.2020.10.008]
19. [19] M. Deng, Z. Li, Y. Kang, C. L.P. Chen, and X. Chu, "A learning-based hierarchical control scheme for an exoskeleton robot in human-robot cooperative manipulation," IEEE Transactions on Cybernetics, vol. 50, no. 1, pp. 112-125, 2020. [DOI:10.1109/TCYB.2018.2864784]
20. [20] J. Sun, J. Wang, P. Yang, and S. Guo, "Model-free prescribed performance fixed-time control for wearable exoskeletons," Applied Mathematical Modelling, vol. 90, no. 1, pp. 61-77, 2021. [DOI:10.1016/j.apm.2020.09.010]
21. [21] B. Brahmi, M. Saad, M.H. Rahman, and C.O. Luna, "Cartesian trajectory tracking of a 7-DOF exoskeleton robot based on human inverse kinematics," IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Human, vol. 49, no. 3, pp. 600-611, 2019. [DOI:10.1109/TSMC.2017.2695003]
22. [22] W. He, Z. Li, Y. Dong, and Ting Zhao, "Design and adaptive control for an upper limb robotic exoskeleton in presence of input saturation," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 1, pp. 97-108, 2019. [DOI:10.1109/TNNLS.2018.2828813]
23. [23] R. Sharma, P. Gaur, S. Bhatt, and D. Joshi, "Optimal fuzzy logic-based control strategy for lower limb rehabilitation exoskeleton," Applied Soft Computing, vol. 105, no. 1, pp. 107226 (1-12), 2021. [DOI:10.1016/j.asoc.2021.107226]
24. [24] E. Fazli, S. M. Rakhtala, N. Mirrashid, and H. R. Karimi, "Real-time implementation of a super twisting control algorithm for an upper limb wearable robot," Mechatronics, vol. 84, no. 1, pp. 102808 (1-13), 2022. [DOI:10.1016/j.mechatronics.2022.102808]
25. [25] Q. Wu, B. Chen, and H. Wu, "RBFN-based adaptive backstepping sliding mode control of an upper-limb exoskeleton with dynamic uncertainties," IEEE Access, vol. 7, no. 1, pp. 134635-134646, 2019. [DOI:10.1109/ACCESS.2019.2941973]
26. [26] H. Fakharizade Bafghi, M.R. Jahed-Motlagh, A. Abooee, and A. Moarefianpur, "Robust finite-time tracking for a square fully-actuated class of nonlinear systems," Nonlinear Dynamics, vol. 103, no. 1, pp. 1611-1625, 2021. [DOI:10.1007/s11071-020-06187-0]
27. [27] F. Sedghi, M. M. Arefi, A. Abooee, and S. Yin, "Distributed adaptive-neural finite-time consensus control for stochastic nonlinear multi-agent systems subject to saturated inputs," IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2022.3145975, 2022. [DOI:10.1109/TNNLS.2022.3145975]
28. [28] F. Sedghi, M. M. Arefi, A. Abooee, and O. Kaynak, "Adaptive robust finite-time nonlinear control of a typical autonomous underwater vehicle with saturated inputs and uncertainties," IEEE/ASME Transactions on Mechatronics, vol. 26, no. 5, pp. 2517-2527, 2021. [DOI:10.1109/TMECH.2020.3041613]
29. [29] A. Abooee, M. Hayeri Mehrizi, M. M. Arefi, and S. Yin, "Finite-time sliding mode control for a 3-DOF fully actuated autonomous surface vehicle," Transactions of the Institute of Measurement and Control, vol. 43, no. 2, pp. 371-389, 2021. [DOI:10.1177/0142331220957516]
30. [30] S. Neisarian, M. M. Arefi, A. Abooee, and S. Yin, "Fast finite-time observer-based sliding mode controller design for a class of uncertain nonlinear systems with input saturation," Information Sciences, vol. 630, no. 1, pp. 599-622, 2023. [DOI:10.1016/j.ins.2023.02.051]
31. [31] A. Abooee and M. M. Arefi, "Robust finite time stabilizers for five-degree-of-freedom active magnetic bearing system," Journal of the Franklin Institute, vol. 356, no. 1, pp. 80-102, 2019. [DOI:10.1016/j.jfranklin.2018.08.026]
32. [32] F. Sedghi, M. M. Arefi, and A. Abooee, "Command filtered-based neuro-adaptive robust finite-time trajectory tracking control of autonomous underwater vehicles under stochastic perturbations," Neurocomputing, vol. 519, no. 1, pp. 158-172, 2023. [DOI:10.1016/j.neucom.2022.11.005]
33. [33] H. Li, S. Zhao, W. He, and R. Lu, "Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone," Automatica, vol. 100, no. 1, pp. 99-107, 2019. [DOI:10.1016/j.automatica.2018.10.030]
34. [34] A. Abooee and M. M. Arefi, "Robust finite-time stabilizers for a connected chain of nonlinear double-integrator systems," IEEE Systems Journal, vol. 13, no. 1, pp. 833-841, 2019. [DOI:10.1109/JSYST.2018.2851153]
35. [35] M. Basin, "Finite- and fixed-time convergent algorithms: Design and convergence time estimation," Annual Reviews in Control, vol. 48, no. 1, pp. 209-221, 2019. [DOI:10.1016/j.arcontrol.2019.05.007]
36. [36] علي ابويي، حمیدرضا احمدزاده، محمد حائری و محمد مهدی عارفی "طراحی گشتاورهای غیرخطی زمان-محدود مقاوم برای ربات n-درجه آزادی درحضور نامعینیها و غیرخطیسازهای ورودی شعاعی و ناحیه مرده" مجله علمي و پژوهشي کنترل، جلد 14، شماره 1، بهار 1399، صفحات 73-91.
37. [37] علي ابويي، مهران اسلامی و محمد حائری، "طراحی کنترلکنندههای غیرخطی زمان-محدود مقاوم برای زیردریایی شش درجه آزادی به منظور ردیابی مسیر" مجله علمي و پژوهشي کنترل، جلد 14، شماره 1، بهار 1399، صفحات 93-113.
38. [38] علی ابویی،"ارائهی ساختار کنترلی تلفیقی نوآورانه برای وسیلهی دریایی خودکار تحریک کامل" نشریه سامانههای غیرخطی در مهندسی برق، ﺟﻠﺪ 9، ﺷﻤﺎره 1، بهار و تابستان 1401، ﺻﻔﺤﺎت 118-146.
39. [39] ﻋﻠﯽ اﺑﻮﯾﯽ، فرزاد محمودیان بارزی و محمد حائری،" ردیابی نقطه بیشینه توان در سیستم فتوولتائی دارای نامعینی با رویکردکنترل غیرخطی زمان-متناهی" مجله علمي و پژوهشي کنترل، ﺟﻠﺪ 16، ﺷﻤﺎره 4، زمستان 1401، ﺻﻔﺤﺎت 1-13.
40. [40] S. Song, B. Zhang, J. Xia, and Z. Zhang, "Adaptive backstepping hybrid fuzzy sliding mode control for uncertain fractional-order nonlinear systems based on finite-time scheme," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 4, pp. 1559-1569, 2020. [DOI:10.1109/TSMC.2018.2877042]
41. [41] W. Dou, S. Ding, and X. Chen, "Practical adaptive finite-time stabilization for a class of second-order systems," Applied Mathematics and Computation, vol. 431, no. 1, pp. 127340 (1-14), 2022. [DOI:10.1016/j.amc.2022.127340]
42. [42] W. Lv, J. Lu, Y. Li, Y. Chu, and S. Xu, "Adaptive neural finite-time control of nonlinear systems subject to sensor hysteresis," Journal of the Franklin Institute, vol. 359, no. 7, pp. 2932-2948, 2022. [DOI:10.1016/j.jfranklin.2022.02.032]
43. [43] J A. Abooee, M. Moravej Khorasani, and M. Haeri, "Finite-time control of robotic manipulators with position output feedback," International Journal of Robust and Nonlinear Control, vol. 27, no. 16, pp. 292-2999, 2017. [DOI:10.1002/rnc.3721]
44. [44] T. Yu, H. Wang, J. Cao, and C.F. Xue, "Finite-time stabilization of memristive neural networks via two-phase method," Neurocomputing, vol. 491, no. 1, pp. 24-33, 2022. [DOI:10.1016/j.neucom.2022.03.059]
45. [45] A. Abooee, M. Moravej Khorasani, and M. Haeri, "Global finite-time stabilization of a class of uncertain MIMO nonlinear systems," Journal of Dynamic Systems, Measurement, and Control, vol. 138, no. 2, pp. 021007 (1-9), 2016. [DOI:10.1115/1.4032065]
46. [46] علی ابویی، سجاد مرادی و وحید ابوطالبی،" هدایت زمان-متناهی سوزن جرّاحی رباتیک در بافت پروستات بر اساس رویکرد کنترل غیرخطی مقاوم-تطبیقی "نشریه سامانههای غیرخطی در مهندسی برق، ﺟﻠﺪ 9، ﺷﻤﺎره 2، پاییز و زمستان 1401، ﺻﻔﺤﺎت 27-50.
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