Active Compliant and Adaptive Interaction Control for a Lower Limb Rehabilitation Robot
LIANG Xu1,2, WANG Weiqun2, SU Tingting1, HOU Zengguang2,3,4, HE Guangping1, REN Shixin2,3, SHI Weiguo2,3
1. North China University of Technology, Beijing 100144, China; 2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China; 4. CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
梁旭, 王卫群, 苏婷婷, 侯增广, 何广平, 任士鑫, 石伟国. 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.DOI: 10.13973/j.cnki.robot.210029.
LIANG Xu, WANG Weiqun, SU Tingting, HOU Zengguang, HE Guangping, REN Shixin, SHI Weiguo. Active Compliant and Adaptive Interaction Control for a Lower Limb Rehabilitation Robot. ROBOT, 2021, 43(5): 547-556. DOI: 10.13973/j.cnki.robot.210029.
Abstract：In order to provide patients with a stable, comfortable and active compliant rehabilitation training environment, an active compliant interactive control scheme for a lower limb rehabilitation robot based on adaptive adjustment of impedance parameters is proposed. It consists of two parts:the outer loop for impedance parameter adjustment and the inner loop for trajectory tracking. Firstly, a fuzzy adaptive impedance parameter adjuster is proposed to solve the problem caused by the dynamic change of impedance parameters of human body during rehabilitation training. It takes the human-robot interaction force, the position error and the speed error as inputs, and uses the fuzzy inference to adjust the damping and stiffness coefficients in real time to realize the self-adaptation to human body impedance. Secondly, an indirect adaptive fuzzy controller is designed, in which the fuzzy system is reasonably constructed to approximate the unknown nonlinear system, and the set trajectory reflecting the patient's motion intention is tracked stably. The system stability is proved by Lyapunov method. Finally, the simulation results show that the maximum deviations of the hip and knee trajectories under the proposed method are reduced by 53.43% and 66.87% respectively in comparison with the general method of fixed expected impedance parameters, which verifies the feasibility and effectiveness of the proposed method.
[1] Wolfe C D A. The impact of stroke[J]. British Medical Bulletin, 2000, 56(2):275-286. [2] 《中国脑卒中防治报告》编写组.《中国脑卒中防治报告2019》概要[J].中国脑血管病杂志, 2020, 17(5):272-281. "Brief report on stroke prevention and treatment in China" writing group. Brief report on stroke prevention and treatment in China, 2019[J]. Chinese Journal of Cerebrovascular Diseases, 2020, 17(5):272-281. [3] 侯增广,赵新刚,程龙,等.康复机器人与智能辅助系统的研究进展[J].自动化学报, 2016, 42(12):1765-1779. Hou Z G, Zhao X G, Cheng L, et al. Recent advances in rehabilitation robots and intelligent assistance systems[J]. Acta Automatica Sinica, 2016, 42(12):1765-1779. [4] Klamroth-Marganska V, Blanco J, Campen K, et al. Threedimensional, task-specific robot therapy of the arm after stroke:A multicentre, parallel-group randomised trial[J]. The Lancet Neurology, 2014, 13(2):159-166. [5] Connell L A, McMahon N E, Harris J E, et al. A formative evaluation of the implementation of an upper limb stroke rehabilitation intervention in clinical practice:A qualitative interview study[J]. Implementation Science, 2014, 9. DOI:10.1186/s13012-014-0090-3. [6] 戴红.康复医学[M].北京:北京大学医学出版社, 2019. Dai H. Rehabilitation medicine[M]. Beijing:Peking University Medical Press, 2019. [7] 杨颐,王雪飞,王麟鹏.神经可塑性与脑卒中后运动功能恢复[J].中国医刊, 2016, 51(6):15-19. Yang Y, Wang X F, Wang L P. Neuroplasticity and motor function recovery after stroke[J]. Chinese Journal of Medicine, 2016, 51(6):15-19. [8] 梁旭,王卫群,侯增广,等.康复机器人的人机交互控制方法[J].中国科学:信息科学, 2018, 48(1):24-46. Liang X, Wang W Q, Hou Z G, et al. Interactive control methods for rehabilitation robot[J]. Scientia Sinica:Informationis, 2018, 48(1):24-46. [9] 王晓峰,李醒,王建辉.基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法[J].自动化学报, 2016, 42(12):1899-1914. Wang X F, Li X, Wang J H. Active interaction exercise control of exoskeleton upper limb rehabilitation robot using model-free adaptive methods[J]. Acta Automatica Sinica, 2016, 42(12):1899-1914. [10] 胡进.坐卧式外骨骼下肢康复机器人的运动训练策略和交互控制方法[D].北京:中国科学院大学, 2014. Hu J. Exercise training and interactive control with a horizontal exoskeleton for lower limb rehabilitation[D]. Beijing:University of Chinese Academy of Sciences, 2014. [11] Yu X B, He W, Li Y N, et al. Bayesian estimation of human impedance and motion intention for human-robot collaboration[J]. IEEE Transactions on Cybernetics, 2021, 51(4):1822-1834. [12] Ibarra J C P, Siqueira A A G. Impedance control of rehabilitation robots for lower limbs, review[C]//Joint Conference on Robotics:SBR-LARS Robotics Symposium and Robocontrol. Piscataway, USA:IEEE, 2014:235-240. [13] Huo W G, Mohammed S, Amirat Y, et al. Active impedance control of a lower limb exoskeleton to assist sit-to-stand movement[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2016:3530-3536. [14] Jamwal P K, Hussain S, Ghayesh M H, et al. Impedance control of an intrinsically compliant parallel ankle rehabilitation robot[J]. IEEE Transactions on Industrial Electronics, 2016, 63(6):3638-3647. [15] Koopman B, van Asseldonk E H, van der Kooij H. Selective control of gait subtasks in robotic gait training:Foot clearance support in stroke survivors with a powered exoskeleton[J]. Journal of NeuroEngineering and Rehabilitation, 2013, 10. DOI:10.1186/1743-0003-10-3. [16] 杜义浩,邱石,谢平,等.下肢康复机器人的自适应人机交互控制策略[J].自动化学报, 2018, 44(4):743-750. Du Y H, Qiu S, Xie P, et al. Adaptive interaction control for lower limb rehabilitation robots[J]. Acta Automatica Sinica, 2018, 44(4):743-750. [17] Meng W, Liu Q, Zhou Z D, et al. Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation[J]. Mechatronics, 2015, 31:132-145. [18] Miranda A B W, Forner-Cordero A. Upper limb exoskeleton control based on sliding mode control and feedback linearization[C]//ISSNIP Biosignals and Biorobotics Conference:Biosignals and Robotics for Better and Safer Living. Piscataway, USA:IEEE, 2013. DOI:10.1109/BRC.2013.6487460. [19] Rahman M H, Ochoa-Luna C, Rahman M J, et al. Forceposition control of a robotic exoskeleton to provide upper extremity movement assistance[J]. International Journal of Modelling, Identification and Control, 2014, 21(4):390-400. [20] Li Z J, Su C Y, Li G L, et al. Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(3):555-566. [21] Huang Z C, Liu J Q, Li Z J, et al. Adaptive impedance control of robotic exoskeletons using reinforcement learning[C]//International Conference on Advanced Robotics and Mechatronics. Piscataway, USA:IEEE, 2016:243-248. [22] Wang W Q, Hou Z G, Cheng L, et al. Toward patients' motion intention recognition:Dynamics modeling and identification of iLeg-An LLRR under motion constraints[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 46(7):980-992. [23] Wang W Q, Shi W G, Hou Z G, et al. Prediction of human voluntary torques based on collaborative neuromusculoskeletal modeling and adaptive learning[J]. IEEE Transactions on Industrial Electronics, 2021, 68(6):5217-5226. [24] 刘金琨.机器人控制系统设计与MATLAB仿真[M].北京:清华大学出版社, 2008. Liu J K. Design of robot control system and MATLAB simulation[M]. Beijing:Tsinghua University Press, 2008. [25] Yoo B K, Ham W C. Adaptive control of robot manipulator using fuzzy compensator[J]. IEEE Transactions on Fuzzy Systems, 2000, 8(2):186-199. [26] Narendra K S, Annaswamy A M. Stable adaptive systems[M]. Englewood Cliffs, USA:Prentice Hall, 1989.
