Flatness tracking control scheme of rehabilitation exoskeleton robot with dynamic uncertainties
Bibliographic record
Abstract
Artificial limbs are new robotic devices created to help stroke victims in the rehabilitation process. In this article, we focus our work on applying passive, active or assistive control strategies to provide a physical assistance and rehabilitation by a 7-degree-of-freedom exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user’s physiological characteristics. The flatness controller combined with time-delay estimation is designed for the 7-degree-of-freedom exoskeleton robot called ETS-MARSE (Ecole de Technologie Supérieure—Motion Assistive Robotic-exoskeleton for Superior Extremity) in order to ensure a passive rehabilitation exercises with a high level of tracking accuracy and robustness against the uncertainty constraints. The stability analysis of such systems is proven using the Lyapunov–Krasovskii functional theory. This approach is illustrated by experimental results with healthy human to highlight the efficiency of the suggested controller scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".