Multiphase-Based Optimal Slip Ratio Tracking Control of Aircraft Antiskid Braking System via Second-Order Sliding-Mode Approach
Bibliographic record
Abstract
This article addresses a novel multiphase-based real-time optimal slip ratio tracking control problem of an aircraft antiskid braking system (ABS) based on the second-order sliding-mode approach. First, a comprehensive dynamic model of an aircraft ABS is established, in which the coupling mechanism of the longitudinal, the vertical, and the pitching dynamics of the aircraft are thoroughly considered. Second, for the aircraft ABS with high nonlinearity, since the measurements of aircraft velocity and acceleration cannot be transmitted to the aircraft ABS in time, the second-order sliding-mode differentiator (SMD) will be designed to estimate them simultaneously. Then, the optimal slip ratio signal, which formulates the maximum friction coefficient, will be updated based on the static friction coefficient model with real-time measured and estimated signals. Furthermore, a novel multiphase-based slip ratio regulation algorithm integrated with the second-order sliding-mode control strategy is proposed on the basis of runway characteristics to track the optimal slip ratio signal, which can not only stop the aircraft faster but prevent the mainwheel from final locking. Finally, the simulation results are presented to demonstrate that the aircraft antiskid braking algorithm proposed in this article can effectively prevent the mainwheel from locking under different runway conditions, and significantly improve the braking efficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".