Fractional order predictive sliding-mode control for a class of nonlinear input-delay systems: singular and non-singular approach
Bibliographic record
Abstract
Nonlinear models of physical systems usually suffer from input delay and external disturbances. Moreover, when a delayed state is in the input signal gain, it can be non-singular or singular. So, designing a robust controller in a nonlinear system with input and state delay, suitable for non-singular and singular input signal gain, is imperative. The main contribution of our study is to design a new state feedback fractional order predictive sliding mode control (FOPSMC) procedure which not only guarantees the stability of a nonlinear system with known constant input and state delay but also controls the output signal to the desired value. Firstly, a predictor is designed for the system to achieve an input-delay-free one. Then, a state feedback FOPSMC is proposed based on a fractional order sliding signal for a nonlinear system with non-singular control gain. Also, a state feedback FOPSMC and a fractional order sliding mode observer (FOSMO) for the virtual disturbance are designed for singular control gain situation. It is proved analytically, through the Lyapunov stability criteria, that both control procedures can stabilise the system and can control the output signal to the desired value, effectively. Finally, the simulation results verify the effectiveness of the analytical achievements.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".