Model-Free Control of Finger Dynamics in Prosthetic Hand Myoelectric-based Control Systems
Bibliographic record
Abstract
This paper proposes an approach to the tuning of model-free controllers for the midcarpal joint angles, which are important finger angles that ensure the desired finger dynamics in prosthetic hand myoelectric-based control systems.The process in these control systems is characterized by fuzzy models that operate with myoelectric signals obtained from eight myoelectric sensors and past inputs and/or outputs, where the controlled outputs are five finger angles.Since the fuzzy models exhibit very good performance as shown in authors' recent papers that produced evolving fuzzy models, they are used to simulate the process behaviour.The Multi Input-Multi Output (MIMO) control system structure consists of five separate Single Input-Single Output control loops with the most simple model-free controllers represented by intelligent Proportional (iP) controllers, separately designed and tuned for each finger.Digital simulation results are included to suggestively illustrate the very good performance of the control systems with iP controllers.The MIMO control system performance is compared with that of the same system but with Proportional-Integral controllers, which are optimally tuned in a model-based manner by a metaheuristic Grey Wolf Optimizer (GWO) algorithm.The fair comparison is ensured by the optimal tuning of the free parameters of iP controllers in a model-based manner using GWO.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".