A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot
Lower limb rehabilitation exoskeleton robots have the characteristics of nonlinearity and strong coupling, and they are easily disturbed during operation by environmental factors. Thus, an accurate dynamic model of the robot is difficult to obtain, and achieving trajectory tracking control of the ro...
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MDPI AG
2021
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oai:doaj.org-article:4d4802635d274ec081ec54d95be629a82021-11-11T15:21:12ZA Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot10.3390/app1121103292076-3417https://doaj.org/article/4d4802635d274ec081ec54d95be629a82021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10329https://doaj.org/toc/2076-3417Lower limb rehabilitation exoskeleton robots have the characteristics of nonlinearity and strong coupling, and they are easily disturbed during operation by environmental factors. Thus, an accurate dynamic model of the robot is difficult to obtain, and achieving trajectory tracking control of the robot is also difficult. In this article, a self-adaptive-coefficient double-power sliding mode control method is proposed to overcome the difficulty of tracking the robot trajectory. The method combines an estimated dynamic model with sliding mode control. A nonlinear control law was designed based on the robot dynamics model and computational torque method, and a compensation term of control law based on double-power reaching law was introduced to reduce the disturbance from model error and environmental factors. The self-adaptive coefficient of the compensation term of the control law was designed to adaptively adjust the compensation term to improve the anti-interference ability of the robot. The simulation and experiment results show that the proposed method effectively improves the trajectory tracking accuracy and anti-interference ability of the robot. Compared with the traditional computed torque method, the proposed method decreases the tracking error by more than 71.77%. The maximum absolute error of the hip joint and knee joint remained below 0.55° and 1.65°, respectively, in the wearable experiment of the robot.Yuepeng ZhangGuangzhong CaoWenzhou LiJiangcheng ChenLinglong LiDongfeng DiaoMDPI AGarticlelower limb rehabilitation exoskeleton robottrajectory trackingestimated dynamic modelsliding mode controlself-adaptive-coefficient-double-power reaching lawTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10329, p 10329 (2021) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
lower limb rehabilitation exoskeleton robot trajectory tracking estimated dynamic model sliding mode control self-adaptive-coefficient-double-power reaching law Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
| spellingShingle |
lower limb rehabilitation exoskeleton robot trajectory tracking estimated dynamic model sliding mode control self-adaptive-coefficient-double-power reaching law Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yuepeng Zhang Guangzhong Cao Wenzhou Li Jiangcheng Chen Linglong Li Dongfeng Diao A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot |
| description |
Lower limb rehabilitation exoskeleton robots have the characteristics of nonlinearity and strong coupling, and they are easily disturbed during operation by environmental factors. Thus, an accurate dynamic model of the robot is difficult to obtain, and achieving trajectory tracking control of the robot is also difficult. In this article, a self-adaptive-coefficient double-power sliding mode control method is proposed to overcome the difficulty of tracking the robot trajectory. The method combines an estimated dynamic model with sliding mode control. A nonlinear control law was designed based on the robot dynamics model and computational torque method, and a compensation term of control law based on double-power reaching law was introduced to reduce the disturbance from model error and environmental factors. The self-adaptive coefficient of the compensation term of the control law was designed to adaptively adjust the compensation term to improve the anti-interference ability of the robot. The simulation and experiment results show that the proposed method effectively improves the trajectory tracking accuracy and anti-interference ability of the robot. Compared with the traditional computed torque method, the proposed method decreases the tracking error by more than 71.77%. The maximum absolute error of the hip joint and knee joint remained below 0.55° and 1.65°, respectively, in the wearable experiment of the robot. |
| format |
article |
| author |
Yuepeng Zhang Guangzhong Cao Wenzhou Li Jiangcheng Chen Linglong Li Dongfeng Diao |
| author_facet |
Yuepeng Zhang Guangzhong Cao Wenzhou Li Jiangcheng Chen Linglong Li Dongfeng Diao |
| author_sort |
Yuepeng Zhang |
| title |
A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot |
| title_short |
A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot |
| title_full |
A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot |
| title_fullStr |
A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot |
| title_full_unstemmed |
A Self-Adaptive-Coefficient-Double-Power Sliding Mode Control Method for Lower Limb Rehabilitation Exoskeleton Robot |
| title_sort |
self-adaptive-coefficient-double-power sliding mode control method for lower limb rehabilitation exoskeleton robot |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/4d4802635d274ec081ec54d95be629a8 |
| work_keys_str_mv |
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