基于数据驱动的膝关节外骨骼控制

doi:10.3785/j.issn.1008-973X.2019.10.020

浙江大学学报(工学版)

2019, Vol. 53

Issue (10): 2024-2033 DOI: 10.3785/j.issn.1008-973X.2019.10.020

自动化技术、计算机技术

基于数据驱动的膝关节外骨骼控制

张燕(

),王建宙,李威,王婕*(

),陈玲玲,杨鹏

河北工业大学人工智能与数据科学学院，天津 300131

Knee-joint exoskeleton control based on data-driven approach

Yan ZHANG(

),Jian-zhou WANG,Wei LI,Jie WANG*(

),Ling-ling CHEN,Peng YANG

School of Artificial Intelligence, Hebei University of Technology, Tianjin 300131, China

全文: PDF(1584 KB) HTML

摘要：

为了识别人体运动意图协调人机运动，采用二维激光测距仪采集地形数据进行在线识别，使用学习向量量化（LVQ）的方法，基于不同地形间的距离特征实现快速、准确的地形分类. 设计基于数据驱动的无模型自适应控制方法，基于膝关节角度的输入输出数据建立动态线性化模型，避免了人机外骨骼建模的复杂性和建模误差. 建立人机外骨骼模型，通过仿真得到正常行走时膝关节的先验力矩，引入先验力矩提高控制器的准确性. 搭建ADAMS和MATLAB联合仿真平台，选取平地路况进行实验. 实验结果表明，所设计的控制方法使得外骨骼膝关节对目标角度有良好的跟踪，对人体行走有较好的助行效果.

关键词： 外骨骼; 地形识别; 数据驱动; 无模型自适应控制; 动力学仿真

Abstract:

The two-dimensional laser rangefinder was used to collect terrain data for online identification in order to identify human movement intentions and coordinate human-exoskeleton motion. The method of learning vector quantization (LVQ) was used based on the distance features between different terrains in order to achieve fast and accurate terrain classification. A model-free adaptive control method based on data drive was designed, and the dynamic linearization model was established based on the input and output data of knee joint angle, which avoided the complexity and error of human-exoskeleton modeling. A human-exoskeleton model was established and the prior torque of the knee joint was obtained through the walking simulation. The prior torque was introduced to improve the accuracy of the controller. The ADAMS-MATLAB co-simulation platform was constructed, and the flat road condition was selected for experiment. The experimental results show that the designed strategy enables the knee-joint exoskeleton to track the trajectory of angle well and has a good performance on walking assistance.

Key words: exoskeleton terrain recognition data-driven model-free adaptive control dynamics simulation

收稿日期: 2018-08-23 出版日期: 2019-09-30

CLC:

TP 242

通讯作者: 王婕 E-mail: yzhangz@163.com;wangjie@hebut.edu.cn

作者简介: 张燕（1974—），女，教授，从事智能算法、智能康复辅具的研究. orcid.org/0000-0002-9727-0212. E-mail： yzhangz@163.com

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引用本文:

张燕,王建宙,李威,王婕,陈玲玲,杨鹏. 基于数据驱动的膝关节外骨骼控制[J]. 浙江大学学报(工学版), 2019, 53(10): 2024-2033.

Yan ZHANG,Jian-zhou WANG,Wei LI,Jie WANG,Ling-ling CHEN,Peng YANG. Knee-joint exoskeleton control based on data-driven approach. Journal of ZheJiang University (Engineering Science), 2019, 53(10): 2024-2033.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.10.020 或 http://www.zjujournals.com/eng/CN/Y2019/V53/I10/2024

图 1 膝关节外骨骼控制系统结构图

图 2 二维激光测距仪

表 1 二维激光测距仪参数

图 3 二维激光测距仪的扫描范围与识别距离

图 4 人体标记点位置与VICON步态分析系统

图 5 传感器采集的5种地形数据

图 6 人体下肢及外骨骼模型

图 7 膝关节仿真先验力矩

表 2 4种方法的识别率比较

图 8 4种方法的平均识别时间比较

表 3 人体模型参数

图 9 角度驱动的人体步态仿真图

图 10 髋关节期望角度与仿真角度的对比

图 11 膝关节期望角度与仿真角度对比

图 12 踝关期望节角度与仿真角度对比

图 13 穿戴外骨骼的仿真步态图

图 14 含先验力矩的MFAC和MFAC方法的膝关节角度跟随效果比较

图 15 含先验力矩的MFAC和MFAC方法的膝关节角度跟随误差比较

图 16 仿真先验力矩与控制力矩的比较

1	KAZEROONI H, STEGER R The Berkeley lower extremity exoskeleton[J]. Journal of Dynamic Systems, Measurement and Control, 2006, 128 (1): 14- 25 doi: 10.1115/1.2168164
2	WALSH C J, ENDO K, HERR H A quasi-passive leg exoskeleton for load-carrying augmentation[J]. International Journal of Humanoid Robotics, 2007, 4 (3): 487- 506 doi: 10.1142/S0219843607001126
3	TSUKAHARA A, HASEGAWA Y, EGUCHI K, et al Restoration of gait for spinal cord injury patients using HAL with intention estimator for preferable swing speed[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23 (2): 308- 318 doi: 10.1109/TNSRE.2014.2364618
4	杨巍, 张秀峰, 杨灿军, 等基于人机5杆模型的下肢外骨骼系统设计[J]. 浙江大学学报: 工学版, 2014, 48 (3): 430- 435 YANG Wei, ZHANG Xiu-feng, YANG Can-jun, et al Design of a lower extremity exoskeleton based on 5-bar human machine model[J]. Journal of Zhejiang University: Engineering Science, 2014, 48 (3): 430- 435
5	刘磊, 杨鹏, 刘作军基于多源信息和广义回归神经网络的下肢运动模式识别[J]. 机器人, 2015, 37 (3): 310- 317 LIU Lei, YANG Peng, LIU Zuo-jun Lower limb locomotion modes recognition based on multiple-source information and general regression neural network[J]. Robot, 2015, 37 (3): 310- 317
6	KRAUSZ N E, LENZI T, HARGROVE L J Depth sensing for improved control of lower limb prostheses[J]. IEEE Transactions on Biomedical Engineering, 2015, 62 (11): 2576- 2587 doi: 10.1109/TBME.2015.2448457
7	路永乐, 张欣, 龚爽, 等基于MEMS惯性传感器的人体多运动模式识别[J]. 中国惯性技术学报, 2016, 24 (5): 589- 594 LU Yong-le, ZHANG Xin, GONG Shuang, et al Recognition of multiple human motion patterns based on MEMS inertial sensors[J]. Journal of Chinese Inertial Technology, 2016, 24 (5): 589- 594
8	张燕, 许京, 陈玲玲, 等基于激光距离传感器的路况识别系统的设计[J]. 激光与红外, 2016, 46 (3): 265- 270 ZHANG Yan, XU Jing, CHEN Ling-ling, et al Design of terrain recognition system based on laser distance sensor[J]. Laser and Infrared, 2016, 46 (3): 265- 270 doi: 10.3969/j.issn.1001-5078.2016.03.004
9	肖熙, 周路基于k均值和基于归一化类内方差的语音识别自适应聚类特征提取算法法[J]. 清华大学学报: 自然科学版, 2017, 57 (8): 857- 861 XIAO Xi, ZHOU Lu Speech recognition adaptive clustering feature extraction algorithms based on the k-means algorithm and the normalized intra-class variance[J]. Journal of Tsinghua University: Science and Technology, 2017, 57 (8): 857- 861
10	PRATT J E, KRUPP B T, MORSE C J, et al. The RoboKnee: an exoskeleton for enhancing strength and endurance during walking [C]//IEEE International Conference on Robotics and Automation. New Orleans: IEEE, 2004: 2430-3435.
11	RIFAI H, MOHAMMED S, DJOUANI K, et al Toward lower limbs functional rehabilitation through a knee-joint exoskeleton[J]. IEEE Transactions on Control Systems Technology, 2017, 25 (2): 712- 719 doi: 10.1109/TCST.2016.2565385
12	CHEN X, ZENG Y, YIN Y H Improving the transparency of an exoskeleton knee joint based on the understanding of motor intent using energy kernel method of EMG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25 (6): 577- 588 doi: 10.1109/TNSRE.2016.2582321
13	韩亚丽, 许有熊, 高海涛, 等基于导纳控制的膝关节外骨骼摆动控制研究[J]. 自动化学报, 2016, 42 (12): 1944- 1950 HAN Ya-li, XU You-xiong, GAO Hai-tao, et al Knee joint exoskeleton swing control with admittance control[J]. Acta Automatica A Sinica, 2016, 42 (12): 1944- 1950
14	ZHU Y M, HOU Z S, QIAN F, et al Dual RBFNNs-based model-free adaptive control with aspen HYSYS simulation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28 (3): 759- 765 doi: 10.1109/TNNLS.2016.2522098
15	BU X H, HOU Z S, ZHANG H W Data-driven multiagent systems consensus tracking using model free adaptive control[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (5): 1514- 1524 doi: 10.1109/TNNLS.2017.2673020
16	XU D Z, SHI Y, JI Z C Model-free adaptive discrete-time integral sliding-mode-constrained-control for autonomous 4WMV parking systems[J]. IEEE Transactions on Industrial Electronics, 2018, 65 (1): 834- 843 doi: 10.1109/TIE.2017.2739680
17	WANG Z W, NASSER M N, HUANG T S Spatial-spectral classification of hyperspectral images using discriminative dictionary designed by learning vector quantization[J]. IEEE Geoscience and Remote Sensing Society, 2013, 52 (8): 4808- 4822
18	YANG H T, HUANG C M, HUANG Y C, et al A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output[J]. IEEE Transactions on Sustainable Energy, 2014, 5 (3): 917- 926 doi: 10.1109/TSTE.2014.2313600
19	EKRAMIAN M, SHEIKHOLESLAM F, HOSSEINNIA S, et al Adaptive state observer for lipschitz nonlinear systems[J]. Systems and Control Letters, 2013, 62 (4): 319- 323 doi: 10.1016/j.sysconle.2013.01.002
20	HOU Z S, JIN S T Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems[J]. IEEE Transactions on Neural Networks, 2011, 22 (12): 2173- 2188 doi: 10.1109/TNN.2011.2176141
21	HOU Z S, ZHU Y M Controller-dynamic-linearization- based model free adaptive control for discrete-time nonlinear systems[J]. IEEE Transactions on Industrial Informatics, 2013, 9 (4): 2301- 2309 doi: 10.1109/TII.2013.2257806
22	HOU Z S, JIN S T A novel data-driven control approach for a class of discrete-time nonlinear systems[J]. IEEE Transactions on Control Systems Technology, 2011, 19 (6): 1549- 1558 doi: 10.1109/TCST.2010.2093136
23	黄萍, 钟慧敏, 陈博正常青年人三维步态: 时空及运动学和运动力学参数分析[J]. 中国组织工程研究, 2015, 19 (24): 3882- 3888 HUANG Ping, ZHONG Hui-min, CHEN Bo Three- dimensional gait analysis in normal young adults: temporal, kinematic and mechanical parameters[J]. Chinese Journal of Tissue Engineering Research, 2015, 19 (24): 3882- 3888 doi: 10.3969/j.issn.2095-4344.2015.24.021

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