Modeling and Control of a Novel Design of Series Elastic Actuator for Upper Limb Rehabilitation
Bibliographic record
Abstract
The field of rehabilitation robotics for upper limb assistance has grown rapidly in the past few years. Rehabilitation robots have direct contact with human joints, which presents a serious safety concern. In this paper, a novel design for a series elastic actuator (SEA) is presented for upper limb rehabilitation of sensorimotor dysfunctions. The proposed SEA ensures safety and robust torque control under various disturbances. The proposed SEA is modeled and simulated using MATLAB Simulink to obtain Input/Output driven data. For the proposed system, two modeling approaches, ARMAX and NN, are used along with comparative analysis for each model fitting. After SEA dynamic modeling, the PSO-tuned LQR controller is designed and implemented. Comparison parameters are then chosen to test the controller behavior during different applied disturbances. The chosen controller showed optimum set-point tracking performance, which is equivalent to upper limb movement. This research can be extended to develop and implement a test bench for upper limb rehabilitation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".