Event-based fast terminal sliding mode control design for a class of uncertain nonlinear systems with input delay: A quantized feedback control
Bibliographic record
Abstract
This paper deals with the problem of regulation and tracking tasks for nonlinear dynamic systems that use a network medium to transmit state measurements. A novel quantized event-triggered fast terminal sliding mode controller (SMC) is proposed to reduce communication resources and computation loads while increase robustness against packet dropout, uncertainties, and disturbances. Then, new criteria are defined for the dynamic quantizer based on the event-triggering error, which increases the accuracy and facilitates the implementation procedures. In practice, delay-free systems are not realistic considerations, thus, their stability is analyzed in the presence of a dynamic quantizer and input delay under the proposed controller scheme. Then, the minimum inter-sampling time is derived, which guarantees the Zeno-free behavior and provides information about the cyber layer bandwidth in cyber-physical systems. Finally, three simulations on an unstable numerical model, the model of an inertia wheel inverted pendulum, and the mass-spring-damper model validate the effectiveness of the proposed methodology.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".