Robust Adaptive Tracking Control of Uncertain Rehabilitation Exoskeleton Robot
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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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 | 0.001 | 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 | 0.000 |
| 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".