Adaptive and Neural Network-based Control Methods Comparison using different Human Torque Synthesis for Upper-limb Robotic Exoskeletons
Bibliographic record
Abstract
The unprecedented and exponentially growing global senior population is creating an exorbitant and unmet demand for physical rehabilitation. Telerehabilitation with robotic exoskeletons is an emerging, and compelling complementary rehabilitation modality. Some challenges are to overcome the effects of dynamic modeling uncertainties and ensure good tracking performance, stability, safe and compliant motion, and a high degree of telepresence between the two remotely-separated human-robot systems in the presence of nonlinearities, human torques, and communication constraints such as time delays. Two control methods were developed: Adaptive Robust Integral Impedance model (ARII) control and Adaptive Robust Integral Radial Basis Function Neural Networks-based Impedance model (RBFNN-I) control. Both methods implement compliant behaviour using an adjustable impedance model and revealed desirable performance. A novel human torque regulator (HTR) was developed, which provides higher fidelity telepresence for the therapist compared to existing methods to enhance the safety and perception of the closed-loop physical interaction. Unilateral and bilateral simulations were carried out using two-degrees-of-freedom (2-DOF) exoskeletons models and experiments were performed using single-joint robots. Excellent tracking performance, telepresence, and stability was achieved in the presence of large, variable and asymmetric time delays and human torques under numerous parameters variations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".