Variable Admittance Control of Upper Limb Rehabilitation Robot Based on Fuzzy Control

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Variable Admittance Control of Upper Limb Rehabilitation Robot Based on Fuzzy Control

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[1]GAO Jianshe,LIU Luqi,WANG Jie,et al.Variable Admittance Control of Upper Limb Rehabilitation Robot Based on Fuzzy Control[J].Journal of Zhengzhou University (Engineering Science),2024,45(01):12-20.[doi:10.13705/j.issn.1671-6833.2024.01.002]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 01 Page number: 12-20 Column: Public date: 2024-01-19

Title:: Variable Admittance Control of Upper Limb Rehabilitation Robot Based on Fuzzy Control

Author(s):: GAO Jianshe; LIU Luqi; WANG Jie; LI Xuexiao; DING Shunliang; GAO Yiyang; WANG Xuan; 1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China;2. Anyang Xinsheng Machine Tool Co. , Ltd. , Anyang 455000, China

Keywords:: admittance control; fuzzy control; human-computer interaction force; upper limb rehabilitation robot; rehabilitation training

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2024.01.002

Abstract:: Traditional fixed-parameter admittance models could not adjust the compliance of upper limb rehabilitation robots in real-time, existing variable-parameter admittance models required improvement based on actual rehabilitation needs. To address this issue, a novel variable-admittance control strategy was proposed for upper limb rehabilitation robots with a self-developed series-parallel hybrid end-effector traction structure, combining fuzzy and admittance control and tailored to actual rehabilitation needs. Four fuzzy rules that could benefit rehabilitation efficiency and safety were developed. This strategy proposed the use of interaction force error and its rate of change as inputs to fuzzy control, to adjust admittance model parameters and achieve autonomous compliance control in real time. Simulation and experimental results validated the feasibility of the proposed variable-admittance control strategy and the effectiveness of the developed four fuzzy rules. In scenarios where patients could adapt to training intensity, the variable-admittance model could reduce the redundant path generated during path tracking by up to 56. 13% thus improving rehabilitation training efficiency. When rehabilitation movements exceeded the patients′ tolerance limit, the variable-admittance model could change the tracking path half a second earlier, improving rehabilitation safety

References:: [1] HARA Y. Brain plasticity and rehabilitation in stroke patients[J].Journal of Nippon Medical School, 2015, 82(1)： 4-13.
[2] ZUO S P, LI J F, DONG M J, et al. Optimum design and preliminary experiments of a novel parallel end traction apparatus for upper-limb rehabilitation[J].Frontiers of Mechanical Engineering, 2021, 16(4)： 726-746.
[3] AGGOGERI F, MIKOLAJCZYK T, O′KANE J. Robotics for rehabilitation of hand movement in stroke survivors[J].Advances in Mechanical Engineering, 2019, 11(4)： 1-14.
[4] TURNER D L, TANG X J, WINTERBOTHAM W, et al. Recovery of submaximal upper limb force production is correlated with better arm position control and motor impairment early after a stroke[J].Clinical Neurophysiology, 2012, 123(1)： 183-192.
[5] WANG S S, LIAO J, YONG Z R, et al. Inertial sensor-based upper limb rehabilitation auxiliary equipment and upper limb functional rehabilitation evaluation[C]∥CCF Conference on Computer Supported Cooperative Work and Social Computing. Cham： Springer, 2022： 518-528.
[6] CALANCA A, MURADORE R, FIORINI P. A review of algorithms for compliant control of stiff and fixed-comp-liance robots[J].IEEE/ASME Transactions on Mechatronics, 2016, 21(2)： 613-624.
[7] HOGAN N. Impedance control： an approach to manipulation[J].Journal of Dynamic Systems, Measurement, and Control,1985,107：1-7.
[8] BAI J, SONG A G, WANG T, et al. A novel backstepping adaptive impedance control for an upper limb rehabilitation robot[J].Computers &Electrical Engineering, 2019, 80： 1-13.
[9] SADO F, SIDEK S N, YUSOF H M. Adaptive hybrid impedance control for a 3DOF upper limb rehabilitation robot using hybrid automata[C]∥2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES). Piscataway： IEEE, 2014： 596-601.
[10] RAGHAVAN P. Upper limb motor impairment after stroke[J].Physical Medicine and Rehabilitation Clinics of North America, 2015, 26(4)： 599-610.
[11] 吴青聪, 王兴松, 吴洪涛, 等. 上肢康复外骨骼机器人的模糊滑模导纳控制[J].机器人, 2018, 40(4)： 457-465.WU Q C, WANG X S, WU H T, et al. Fuzzy sliding mode admittance control of the upper limb rehabilitation exoskeleton robot[J].Robot, 2018, 40(4)： 457-465.
[12] AGUIRRE-OLLINGER G, COLGATE J E, PESHKIN M A, et al. Active-impedance control of a lower-limb assistive exoskeleton[C]∥2007 IEEE 10th International Conference on Rehabilitation Robotics. Piscataway： IEEE, 2007： 188-195.
[13] 乐宇倚, 郭帅. 基于变导纳控制的上肢康复机器人柔顺控制方法研究[J].工业控制计算机, 2021, 34(10)： 9-11, 14.LE Y Y, GUO S. Research on compliance control method of upper-limb rehabilitation robot based on variable admittance control[J].Industrial Control Computer, 2021, 34(10)： 9-11, 14.
[14] 梁旭, 王卫群, 苏婷婷, 等. 下肢康复机器人的主动柔顺自适应交互控制[J].机器人, 2021, 43(5)： 547-556.LIANG X, WANG W Q, SU T T, et al. Active compliant and adaptive interaction control for a lower limb rehabilitation robot[J].Robot, 2021, 43(5)： 547-556.
[15] LI X M, YANG Q Q, SONG R. Performance-based hybrid control of a cable-driven upper-limb rehabilitation robot[J].IEEE Transactions on Biomedical Engineering, 2021, 68(4)： 1351-1359.
[16] 高建设, 王玉闯, 刘德平, 等. 新型四足步行机器人串并混联腿的轨迹规划与仿真研究[J].郑州大学学报(工学版), 2018, 39(2)： 23-27, 38.GAO J S, WANG Y C, LIU D P, et al. Research on trajectory planning and simulation on the serial-parallel leg of a novel quadruped walking robot[J].Journal of Zhengzhou University (Engineering Science), 2018, 39(2)： 23-27, 38.
[17] 李朋阳, 高建设, 顾昌利. 一种串并混联的上肢康复机器人轨迹规划研究[J].机械设计与制造, 2022(6)： 265-269, 273.LI P Y, GAO J S, GU C L. Research on the trajectory planning of a series-parallel upper limb rehabilitation robot[J].Machinery Design &Manufacture, 2022(6)： 265-269, 273.
[18] SERAJI H, COLBAUGH R. Force tracking in impedance control[J].International Journal of Robotics Research, 1997, 16(1)： 97-117.
[19] 檀盼龙, 李益敏, 赵相宾, 等. 线性扩张状态观测滤波器的分析与应用[J].郑州大学学报(工学版), 2019, 40(2)： 41-47.TAN P L, LI Y M, ZHAO X B, et al. Analysis and application of linear extended state observer filter[J].Journal of Zhengzhou University (Engineering Science), 2019, 40(2)： 41-47.
[20] 赵相瑜, 缪志农. 模糊自适应控制中的参数调节[J].中国测试技术, 2004, 30(4)： 24-26.ZHAO X Y, MIAO Z L. Parameters adjusting of adaptive fuzzy control[J].China Measurement Technology, 2004, 30(4)： 24-26.
[21] BOSECKER C, DIPIETRO L, VOLPE B, et al. Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke[J].Neurorehabilitation and Neural Repair, 2010, 24(1)： 62-69.
[22] 胡寿松. 自动控制原理[M].6版. 北京：科学出版社, 2013： 71-88.HU S S. Principle of automatic control[M].6th ed. Beijing： Science Press, 2013： 71-88.
[23] SUZUKI K, MIZUSHIMA S. Rehabilitation technology[M].New York： Elsevier, 1987： 432-694.
[24] LIU C G, HE Y, CHEN X A, et al. Discontinuous force-based robot adaptive switching update rate impedance control[C]∥2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Piscataway： IEEE, 2021： 2573-2580.

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Last Update: 2024-01-23