An LWPR-Based Method for Intelligent Lower-Limb Prosthesis Control by Learning the Dynamic Model in Real Time
Abstract
:1. Introduction
2. Materials and Methods
2.1. Phase Variable Selection
2.2. Motion Mode Recognizer
2.3. Reference Trajectory Generation
2.4. Controller Design
- (1)
- During swing phase
- (2)
- During stance phase
2.5. Learning Policy of LWPR Model
2.5.1. LWPR Model Configuration
2.5.2. Model Learning Policy
3. Results
3.1. Single Motor Experiment
3.2. Hanging Up Experiment
3.3. Leg Swinging Experiment
3.4. Walking Experiment
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Parameters | Value |
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0 | |
0 | |
0 | |
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Parameters | Value (Knee) | Value (Knee) |
---|---|---|
init_D | 50 | 50 |
w_gen | 0.2 | 0.2 |
init_alpha | 250 | 150 |
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Liu, Y.; An, H.; Ma, H.; Wei, Q. An LWPR-Based Method for Intelligent Lower-Limb Prosthesis Control by Learning the Dynamic Model in Real Time. Machines 2023, 11, 186. https://doi.org/10.3390/machines11020186
Liu Y, An H, Ma H, Wei Q. An LWPR-Based Method for Intelligent Lower-Limb Prosthesis Control by Learning the Dynamic Model in Real Time. Machines. 2023; 11(2):186. https://doi.org/10.3390/machines11020186
Chicago/Turabian StyleLiu, Yi, Honglei An, Hongxu Ma, and Qing Wei. 2023. "An LWPR-Based Method for Intelligent Lower-Limb Prosthesis Control by Learning the Dynamic Model in Real Time" Machines 11, no. 2: 186. https://doi.org/10.3390/machines11020186