Control of a 7-DOF Exoskeleton for Upper-Limb Rehabilitation using a Fast Terminal Super-Twisting
Bibliographic record
Abstract
This paper tackles both the singularity problem in fast terminal variable structure control and the problem of accurate tracking for an exoskeleton robotic system suffering from uncertainties. Indeed, this work presents a fast terminal super-twisting with new stability conditions. Firstly, a new power based on the sign of the tracking error variable is used in the fast terminal switching surface to bypass the problem of singularity. Secondly, for the problem of high accuracy tracking, the controller based on standard super-twisting will be designed with a new stability condition that ensures a finite-time stability of the proposed nonlinear switching function to zero. Besides the fact that the proposed method allows the rejection of the un-certain dynamics and perturbations, it also allows the reduction of the chattering. The proposed control technique is applied in simulation and in real-time on a three-link ANAT robot and on a redundant 7-DOF upper-limb wearable robot, respectively. The results of the simulations and the practical implementation are exposed to reveal the efficiency of the developed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".