Wearable exoskeleton robot control using radial basis function‐based fixed‐time terminal sliding mode with prescribed performance
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper tackles the problem of robust and accurate fixed‐time tracking in human–robot interaction and deals with uncertainties. This work introduces a control approach for a wearable exoskeleton designed specifically for rehabilitation tasks. The approach combines prescribed performance control‐based fixed‐time terminal sliding mode with a neural network. Its main objectives are to achieve trajectory tracking, reduce chattering, ensure fixed‐time stability, and maintain robustness against uncertainties. The controller includes a radial basis function neural network to estimate unknown dynamics and incorporates prescribed performance criteria. This enables precise joint space trajectory tracking, even in the presence of uncertain dynamics and disturbances. The prescribed performance ensures that the trajectory tracking error evolves within prescribed limits. The combined neural network and fixed‐time terminal sliding mode technique are proposed to ensure robustness and fixed‐time convergence. The closed‐loop stability is analyzed using the Lyapunov theory, and a new fixed‐time convergence is provided. Numerical simulations demonstrate a reduction tracking compared to another advanced SMC technique, while experimental results on a 7‐DoF ETS‐MARSE exoskeleton show better tracking with control torques free of chattering compared with two advanced SMC techniques.
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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 | 0.000 | 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