Adaptive Force and Position Control Based on Quasi-Time Delay Estimation of Exoskeleton Robot for Rehabilitation
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Bibliographic record
Abstract
Rehabilitation robots have become an influential tool in physical therapy treatment since they are able to provide an intensive rehabilitation treatment for a long period of time. However, this technology still suffers from various problems such as dynamics uncertainties, external disturbances, and human-robot interaction. In this paper, we present a new integral second-order terminal sliding mode control incorporating quasi-time delay estimation (Q-TDE) applied to an exoskeleton robot with dynamics uncertainties and unknown bounded disturbances. Unlike the conventional TDE approach, the proposed Q-TDE uses delayed one step only of the control input of the system to approximate the uncertain dynamics while avoiding the delays on all states of the system. The proposed controller aims to perform passive and active rehabilitation protocols without the need for velocity and acceleration measurements of the robot system. A finite time of both selected sliding surface and estimation error simultaneous is achieved using an appropriate Lyapunov function. Experimental results with healthy subjects found using a virtual reality environment confirm the effectiveness of the proposed control.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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