Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton
Robotic rehabilitation of the lower limb exoskeleton following neurological injury has proven to be an effective rehabilitation technique. Developing assistive control strategies that achieve rehabilitative movements can increase the potential for the recovery of the motor coordination of the partic...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/11/1064 |
| _version_ | 1827644292756144128 |
|---|---|
| author | Haimin He Ruru Xi Youping Gong |
| author_facet | Haimin He Ruru Xi Youping Gong |
| author_sort | Haimin He |
| collection | DOAJ |
| description | Robotic rehabilitation of the lower limb exoskeleton following neurological injury has proven to be an effective rehabilitation technique. Developing assistive control strategies that achieve rehabilitative movements can increase the potential for the recovery of the motor coordination of the participants. In this paper, the innovative contributions are to investigate a robust sliding mode controller (SMC) with radials basis function neural network algorithm (RBFNN) compensator for a novel compliance tendon–sheath actuation lower limb exoskeleton (CLLE) to provide intrinsic thigh and shank rehabilitation training. The controller employing the RBFNN compensator is proposed to reduce the impact of friction from the compliance tendon–sheath actuation system (CTSA). In the design of the compensator, a single parameter is investigated to replace the weight information of the neural network. Our proposed controller is shown to yield fast, stable, and accurate control performance regardless of uncertainties interaction. Two additional algorithms, including a robust adaptive sliding mode controller (RASMC) and a sliding mode proportional-integral controller (SMPIC), are introduced in this paper for comparison. The simulations were presented with MATLAB/SIMULINK to validate the superiority of the performance of the proposed controller. |
| first_indexed | 2024-03-09T18:12:46Z |
| format | Article |
| id | doaj.art-66b2a3d3977b44d1b89df07f51053248 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T18:12:46Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-66b2a3d3977b44d1b89df07f510532482023-11-24T08:58:51ZengMDPI AGMachines2075-17022022-11-011011106410.3390/machines10111064Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb ExoskeletonHaimin He0Ruru Xi1Youping Gong2School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaRobotic rehabilitation of the lower limb exoskeleton following neurological injury has proven to be an effective rehabilitation technique. Developing assistive control strategies that achieve rehabilitative movements can increase the potential for the recovery of the motor coordination of the participants. In this paper, the innovative contributions are to investigate a robust sliding mode controller (SMC) with radials basis function neural network algorithm (RBFNN) compensator for a novel compliance tendon–sheath actuation lower limb exoskeleton (CLLE) to provide intrinsic thigh and shank rehabilitation training. The controller employing the RBFNN compensator is proposed to reduce the impact of friction from the compliance tendon–sheath actuation system (CTSA). In the design of the compensator, a single parameter is investigated to replace the weight information of the neural network. Our proposed controller is shown to yield fast, stable, and accurate control performance regardless of uncertainties interaction. Two additional algorithms, including a robust adaptive sliding mode controller (RASMC) and a sliding mode proportional-integral controller (SMPIC), are introduced in this paper for comparison. The simulations were presented with MATLAB/SIMULINK to validate the superiority of the performance of the proposed controller.https://www.mdpi.com/2075-1702/10/11/1064exoskeleton robotrobust controlneural networkcompliance tendon–sheath actuationuncertainties and friction |
| spellingShingle | Haimin He Ruru Xi Youping Gong Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton Machines exoskeleton robot robust control neural network compliance tendon–sheath actuation uncertainties and friction |
| title | Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton |
| title_full | Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton |
| title_fullStr | Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton |
| title_full_unstemmed | Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton |
| title_short | Performance Analysis of a Robust Controller with Neural Network Algorithm for Compliance Tendon–Sheath Actuation Lower Limb Exoskeleton |
| title_sort | performance analysis of a robust controller with neural network algorithm for compliance tendon sheath actuation lower limb exoskeleton |
| topic | exoskeleton robot robust control neural network compliance tendon–sheath actuation uncertainties and friction |
| url | https://www.mdpi.com/2075-1702/10/11/1064 |
| work_keys_str_mv | AT haiminhe performanceanalysisofarobustcontrollerwithneuralnetworkalgorithmforcompliancetendonsheathactuationlowerlimbexoskeleton AT ruruxi performanceanalysisofarobustcontrollerwithneuralnetworkalgorithmforcompliancetendonsheathactuationlowerlimbexoskeleton AT youpinggong performanceanalysisofarobustcontrollerwithneuralnetworkalgorithmforcompliancetendonsheathactuationlowerlimbexoskeleton |