Robust Adaptive Tracking Control of Uncertain Rehabilitation Exoskeleton Robot
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Rehabilitation robots have become an influential tool in physiotherapy treatment because they are able to provide intensive rehabilitation treatment over a long period of time. However, this technology still suffers from various problems such as dynamic uncertainties, external disturbances, and human–robot interaction. In this paper, we propose a robust adaptive control approach of an exoskeleton robot with an unknown dynamic model and external disturbances. First, the dynamics of the exoskeleton's arm is presented. Then, we design a robust adaptive sliding mode control in which the parameter uncertainties and the disturbances are estimated by the adaptive update methods. The proposed control ensures a good tracking of the system with a finite time convergence of sliding surface to zero. Throughout this paper, the designed control law and the global stability analysis are formulated and demonstrated based on the appropriate choice of the candidate Lyapunov function. The experimental and comparative results, performed for seven degrees-of-freedom (DOFs) exoskeleton arm with three healthy subjects to track a passive rehabilitation motion, confirm the effectiveness and robustness of the proposed control law compared with conventional adaptive approach.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it