Genetic Algorithm-Based Sliding Mode Control of a Human Arm Model
Bibliographic record
Abstract
Spinal cord injured patients cannot move their segments by their intact muscles. A suitable controller can be used to help them move their arm. In this study, the kinematics and dynamics of right-hand movement are modeled considering planar three links. A genetic algorithm-based sliding mode (GASM) controller is designed to move the human arm model for tracking a desired trajectory in the sagittal plane. The GA is used to tune the convergence rate of the sliding mode controller for having an appropriate tracking performance. The summation of errors is considered as a cost function and GA is proposed to find the controller gains to minimize the difference between the outputs of the model and nominal trajectories. To the best of the author's knowledge, it is for the first time that the GA-sliding mode controller has been used for controlling the human hand so as to have a particular movement. Simulation results are evaluated in upward and downward movements of the human arm to affirm the effectiveness of the proposed controller.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".