Adaptive Sliding Mode Control for Nonlinear Impulsive Time-Delay Hybrid Systems
Bibliographic record
Abstract
This article investigates the adaptive sliding mode control (ASMC) for a class of nonlinear impulsive hybrid systems with time-varying delay, adopting a creative approach that emphasizes the switching perspective. When both impulse and sliding mode control are involved in the time-delay system, ensuring the continuity of the sliding mode function (SMF) and achieving the reachability of system states become crucial challenges that need to be addressed. In view of this, a novel impulse-based SMF is developed such that the impulsive effect can be avoided on sliding surface, and the difficulty of its continuity is also settled at impulsive instants. By using the state augmented approach, the delay-dependent Lyapunov function with switched systems is formulated to guarantee robust stability of the given sliding mode dynamics. Meanwhile, the designed ASMC law is derived to achieve the finite reachability of switching surface for system states. It is shown that the proposed ASMC law offers a high degree of freedom for adjusting the constants in nonlinear assumptions. Finally, comparative studies are conducted to validate the theoretical results and demonstrate their practical applicability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".