Assistance Control of Human-Exoskeleton Integrated System for Balance Recovery Augmentation in Sagittal Plane

被引:5
作者
Hua, Yuxiang [1 ]
Zhu, Yanmei [2 ]
Li, Changle [1 ]
Zhao, Jie [1 ]
Zhu, Yanhe [1 ]
机构
[1] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150001, Peoples R China
[2] Harbin Med Univ, Dept Neurol, Harbin 150086, Peoples R China
基金
国家重点研发计划;
关键词
Regulation; Torque; Exoskeletons; Muscles; Modulation; Lips; Perturbation methods; Balance recovery augmentation; balanced state identification; exoskeleton; virtual potential energy; DESIGN; LIMB; MASS;
D O I
10.1109/TIE.2021.3050363
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article proposed a humanoid balance assistance strategy (BAS) to enhance balance recovery in sagittal plane. Numerical construction of balanced state manifold (BSM) which accounts for active intervention of human ankles is developed to detect imbalance criticality (IC). A human-in-the-loop balance judgment criterion is established to appropriately trigger the external assistance intervention. A unified assistive torque controller based on minimization modulation of virtual potential energy (VPE) towards BSM is designed to augment posture regulation. Exploitation of BAS leads to the following contributions: 1) ICs in 6 typical imbalance situations are accurately detected, with identification error within 23.7%. 2) Traversal imbalanced region of center of mass (CoM) is reduced by 44.2% (with imbalanced position/velocity range (IPR/IVR) by 35.6%/34.3%). 3) Position offset (PO) and velocity impact (VI) at landing collision are reduced by 39.3% and 59.4%. 4) Elapsed time of balance recovery is reduced by 160-580 ms. Experimental verification demonstrates the significant augmentation for balance recovery by BAS.
引用
收藏
页码:528 / 538
页数:11
相关论文
共 50 条
[21]   Gait Planning and Multimodal Human-Exoskeleton Cooperative Control Based on Central Pattern Generator [J].
Kou, Jiange ;
Wang, Yixuan ;
Chen, Zhenlei ;
Shi, Yan ;
Guo, Qing .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2024,
[22]   Precision force control of an underactuated stance leg exoskeleton for human performance augmentation [J].
Chen, Shan ;
Han, Tenghui ;
Dong, Fangfang ;
Lu, Lei ;
Liu, Haijun ;
Tian, Xiaoqing ;
Han, Jiang .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2022, 236 (03) :553-566
[23]   Human-Exoskeleton Interaction During Knee Flexion-Extension Under Different Configurations of Robot Assistance-Resistance [J].
Mosconi, Denis ;
Moreno, Yecid ;
Siqueira, Adriano .
SYNERGETIC COOPERATION BETWEEN ROBOTS AND HUMANS, VOL 2, CLAWAR 2023, 2024, 811 :336-344
[24]   Balance Control for an Active Leg Exoskeleton Based on Human Balance Strategies [J].
Huynh, V. ;
Bidard, C. ;
Chevallereau, C. .
NEW TRENDS IN MEDICAL AND SERVICE ROBOTS: DESIGN, ANALYSIS AND CONTROL, 2018, 48 :197-211
[25]   Heuristic-Based Ankle Exoskeleton Control for Co-Adaptive Assistance of Human Locomotion [J].
Jackson, Rachel W. ;
Collins, Steven H. .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (10) :2059-2069
[26]   Can Momentum-Based Control Predict Human Balance Recovery Strategies? [J].
Bayon, C. ;
Emmens, A. R. ;
Afschrift, M. ;
Van Wouwe, T. ;
Keemink, A. Q. L. ;
van der Kooij, H. ;
van Asseldonk, E. H. F. .
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 28 (09) :2015-2024
[27]   Reference Joint Trajectories Generation of CUHK-EXO Exoskeleton for System Balance in Walking Assistance [J].
Chen, Bing ;
Zhong, Chun-Hao ;
Zhao, Xuan ;
Ma, Hao ;
Qin, Ling ;
Liao, Wei-Hsin .
IEEE ACCESS, 2019, 7 :33809-33821
[28]   Human motion intent learning based motion assistance control for a wearable exoskeleton [J].
Long, Yi ;
Du, Zhi-jiang ;
Wang, Wei-dong ;
Dong, Wei .
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 2018, 49 :317-327
[29]   Proxy-based torque control of motor-driven exoskeletons for safe and compliant human-exoskeleton interaction [J].
Liao, Hongpeng ;
Chan, Hugo Hung-Tin ;
Gao, Fei ;
Zhao, Xuan ;
Liu, Gaoyu ;
Liao, Wei-Hsin .
MECHATRONICS, 2022, 88
[30]   End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning [J].
Zheng, Ranran ;
Yu, Zhiyuan ;
Liu, Hongwei ;
Chen, Jing ;
Zhao, Zhe ;
Jia, Longfei .
IEEE ACCESS, 2023, 11 :102340-102351