Fractional Order Sliding Mode Controller for HBV Epidemic System
Bibliographic record
Abstract
The Hepatitis-B (HBV) epidemic's dynamic can be presented as a compartment model.Determining the HBV epidemic control strategy can be considered a nonlinear feedback control problem.The sliding mode controller (SMC) is an effective feedback control method for controlling the dynamical system under disturbances.Recently, the SMC based on fractional order calculus can provide preferable characteristics for a control system such as robustness and convergence rate.In this study, the HBV epidemic system's control policy is proposed using the fractional order sliding mode controller (FOSMC).The control policy with multiple measures including vaccination, isolation, and treatment is formulated to manipulate the susceptible and the infected subpopulations to the desired level.The Lyapunov-based approach is proven for stability analysis.The control policy is applied to the simulation example to verify the feasibility of the proposed FOSMC method.The simulation results are compared with those of the integer order SMC.By the proposed method, the results reveal that the susceptible and infected subpopulations are driven to the desired levels under disturbances with a higher convergence rate compared to that of the integer one.Moreover, the proposed FOSMC method can reduce the chattering occurrence which is the primary drawback of the SMC 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".