Optimal Robust Control For Tremor Suppression in Parkinson’s Disease
Bibliographic record
Abstract
Deep brain stimulation (DBS) is an effective and promising therapy to control Parkinson’s tremor movement in patients with advanced Parkinson’s disease (PD). This paper proposes a new alternative medication that has several advantages, including compatibility with individual needs and low side effects. There has been a rapid improvement in the literature on the development of the dynamic computational model of neuroscience, alongside the development of DBS. A combination of DBS and model-based control strategies opens up a new vision for Parkinson’s disease treatment. Despite the numerous studies on basal ganglia (BG) modeling, researchers are required to employ adaptive and robust strategies to eliminate Parkinson’s patients’ tremors. This paper proposes a new adaptive optimal fast terminal sliding mode control (AOFTSMC) method to mitigate tremors by tuning GABA thorough DBS. This approach represents finite-time convergence law, a new method to stimulate the inner nuclei of BG in a robust and optimum manner that leads to removing tremors of PD fluctuation signal in the presence of uncertainties. Finally, simulation results of the basal ganglia model under the addressed approach are adopted to demonstrate the effectiveness of the proposed method.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".