A predictive enhanced PID-F control strategy for functional electrical stimulation based on the artemisinin optimization algorithm
Bibliographic record
Abstract
Functional electrical stimulation (FES) has demonstrated efficacy in enhancing comfort and mobility for individuals with paralysis, neurological diseases, and compromised muscle function. It facilitates motions that would otherwise be challenging or unattainable for these persons. Achieving optimal performance from FES systems necessitates the formulation of a strong and efficient control approach. To address this need, this paper proposes a novel controller called predictive proportional-integral-filtered derivative (PPID-F), which incorporates a predictive term into a filtered PID controller. The controller parameters are ideally optimized utilizing a recently devised metaheuristic method known as the artemisinin optimizer (AO). In contrast to traditional approaches in the literature, AO is employed to minimize a specifically designed objective function (OF) that integrates the integral of time-weighted absolute error (ITAE) criteria and the system’s peak response (y peak ). Comparative simulation results show that the proposed AO: PPID-F controller achieves the lowest objective function (OF) value among all evaluated methods. Specifically, it reduces the OF value from 0.4862 (AO: PID-F) to 0.2519, resulting in a 48.19% improvement. Compared to other benchmark controllers, the improvement ranges between approximately 48 and 68%. The effectiveness of the proposed controller is subsequently evaluated against six established approaches reported in the literature. The results demonstrate that the AO: PPID-F controller significantly enhances time-domain performance while preserving satisfactory frequency-domain features in comparison to alternative controller architectures developed for the FES system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".