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Article

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance

by 1,2,3,†, 1,2,4,†, 4, 1,2,4, 1,2,3, 1,2,3 and 1,2,3,*
1
CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China
2
Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3
ShenZhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China
4
Harbin Institute of Technology, School of Mechanical Engineering and Automation, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(15), 4333; https://doi.org/10.3390/s20154333
Received: 30 June 2020 / Revised: 28 July 2020 / Accepted: 2 August 2020 / Published: 4 August 2020
(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)
Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multiple joints in the lower limb is presented in this paper. It is used to assist both hip and knee joints in a single system, the assistance force is directly applied to the hip joint flexion and the knee joint extension, while indirectly to the hip extension also. Based on the biological torque of human walking at three different slopes, a novel strategy is developed to improve the performance of assistance. A parameter optimal iterative learning control (POILC) method is introduced to reduce the error generated due to the difference between the wearing position and the biological features of the different wearers. In order to obtain the metabolic rate, three subjects walked on a treadmill, for 10 min on each terrain, at a speed of 4 km/h under both conditions of wearing and not wearing the soft exoskeleton. Results showed that the metabolic rate was decreased with the increasing slope of the terrain. The reductions in the net metabolic rate in the experiments on the downhill, flat ground, and uphill were, respectively, 9.86%, 12.48%, and 22.08% compared to the condition of not wearing the soft exoskeleton, where their corresponding absolute values were 0.28 W/kg, 0.72 W/kg, and 1.60 W/kg. View Full-Text
Keywords: lower limb assistance; soft exoskeleton; iterative learning control; force tracking; metabolic cost lower limb assistance; soft exoskeleton; iterative learning control; force tracking; metabolic cost
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MDPI and ACS Style

Chen, C.; Zhang, Y.; Li, Y.; Wang, Z.; Liu, Y.; Cao, W.; Wu, X. Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance. Sensors 2020, 20, 4333. https://doi.org/10.3390/s20154333

AMA Style

Chen C, Zhang Y, Li Y, Wang Z, Liu Y, Cao W, Wu X. Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance. Sensors. 2020; 20(15):4333. https://doi.org/10.3390/s20154333

Chicago/Turabian Style

Chen, Chunjie, Yu Zhang, Yanjie Li, Zhuo Wang, Yida Liu, Wujing Cao, and Xinyu Wu. 2020. "Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance" Sensors 20, no. 15: 4333. https://doi.org/10.3390/s20154333

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