| Autor: |
Ehsan Jafari, Abbas Erfanian |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 1908-1919 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1558-0210 |
| DOI: |
10.1109/TNSRE.2022.3188735 |
| Popis: |
One of the major challenges facing functional electrical stimulation (FES) cycling is the design of an automatic control system that addresses the problem of disturbance with unknown bound and time-varying behavior of the muscular system. The previous methods for FES-cycling are based on the system modeling and require pre-adjustment of the control parameters which are based on the model parameters. These will degrade the FES-cycling performance and limit the clinical application of the methods. In this paper, a distributed cooperative control framework, which is based on an adaptive higher-order sliding mode (AHOSM) controller, is proposed for simultaneous control of torque and cadence in FES-cycling. The proposed control system is free-model which does not require any pre-adjustment of the control parameters and does not need the boundary of the disturbance to be known. Another major issue in FES-cycling is the stimulation pattern. In the paper, an automatic pattern generator is proposed which is capable of providing not only the regions of the crank angle in which each muscle group should be stimulated but also a specific gain for each muscle group. The results of the simulation studies and experiments on three spinal cord injuries showed that the proposed control strategy significantly increases the efficiency and tracking accuracy of motor-assisted FES-cycling in paraplegic patients and decreases the power consumption compared to HOSM controller with the fixed stimulation pattern. Reducing power consumption can slow down muscle fatigue and consequently increase cycling endurance. The average of cadence and torque tracking errors over three subjects using the proposed method are 5.77± 0.5% and 5.23± 0.8%, respectively. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
