Optimal Utilization of Adhesion Force for Heavy-Haul Electric Locomotive Based on Extremum Seeking with Sliding Mode and Asymmetric Barrier Lyapunov Function
Bibliographic record
Abstract
An optimal utilization of adhesion force based on extremum seeking with sliding mode (SMES) and asymmetric barrier Lyapunov function (ABLF) is proposed for heavy-haul electric locomotives (HHELs), which can eliminate the wheel skidding at optimal adhesion point and achieves maximum traction for HHELs. First, the state equation of wheel-rail adhesion control system is described. The optimal utilization of adhesion force and anti-slip control are analyzed considering the condition changes at the wheel-rail surface. Then, the nonsingular terminal sliding mode observer (NTSMO) is designed to achieve the accurate adhesion coefficient of the wheel-rail. Finally, the SMES method for HHEL is developed to obtain the optimal slip speed and the maximum adhesion coefficient of the uncertain wheel-rail surface. Meanwhile, the ABLF controller is designed to achieve anti-slip control for HHELs in the optimal adhesion state. Comparing with the conventional differential acceleration control (DAC) method, the simulations and experiments verify that the proposed method can achieve optimal adhesion anti-slip control with quick dynamic response, and the HHEL achieves maximum traction.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".