Prescribed-Time Control via Periodic Delayed Feedback-Based Super-Twisting Sliding Mode Algorithm
Bibliographic record
Abstract
This paper investigates the problem of prescribed-time control via continuous sliding mode control laws. First, within the framework of the periodic delayed feedback approach, two novel super-twisting algorithms are proposed, whose settling times are bounded by a constant independent of the initial conditions and can be arbitrarily prescribed. Next, for a class of high-order uncertain nonlinear systems, a novel disturbance observer and a prescribed-time continuous sliding mode control law are designed based on the first super-twisting algorithm. Furthermore, for a class of perturbed double integrator systems, a novel state observer and a prescribed-time continuous sliding mode control law by output feedback are designed based on the second super-twisting algorithm. Unlike the widely used time-varying high-gain feedback approach in the literature, the control laws designed in this paper ensure that the system output converges to zero with the settling time being exactly equal to the prescribed time, even in the presence of both matched and mismatched disturbances, while mitigating sensitivity to measurement noise and avoiding the singularity issues caused by infinite gain. Finally, the effectiveness of the proposed approaches is demonstrated through simulations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".