Adaptive Control of an Upper Extremity Rehabilitation Robot with Backlash
Bibliographic record
Abstract
In Canada over 40,000 new stroke cases are reported annually [1], and it costs the Canadian health care system $3.2 billion a year Thus, new rehabilitation technologies are being investigated. A rehabilitation robot can be used to deliver repetitive practice to the stroke patients, which is the key element for motor recovery Safety issues regarding humanrobot interactions restrict selection of control scenarios. Currently admittance and impedance control approaches and their variations are used to control the rehabilitation robots To implement these control strategies, a complete and accurate dynamic model of the robotic system is required. This issue can be addressed by incorporating robust or adaptive control approaches in the above strategies. For the robust control, if the dynamic uncertainties of the robot are too great, the quality of adaptive assistance or resistance may be compromised during therapy In the adaptive control, for the convergence of the adaption law, a persistently exciting input is required In addition, both adaptive and robust controllers may need high gains when mechanical discontinuities (such as gear backlash) are introduced to the system dynamics. Hence, a precise dynamic model estimation is essential, and can be done through a proper system parameter identification 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".