An Adaptive Nonlinear Differentiable Friction Modeling for Tire-Road Friction Estimation
Bibliographic record
Abstract
In this paper, a differentiable friction model is proposed to estimate the longitudinal tire-road friction force of vehicle systems. A novel adaptive nonlinear observer-based parameter estimation scheme has been developed to estimate the parameters of friction model, which requires the signals from the existing sensors signals such as wheel rotational speed and vehicle speed. Different from conventional gradient and recursive least square (RLS) methods, the filtered regression parameter estimation error is introduced in the novel adaptive laws, which can guarantee the observer error convergence to zero and the estimated parameter also convergence to their real value. The Lyapunov method is used to prove the stability of the proposed methods. The robustness of the developing method against bounded disturbances is also proved. Simulation results illustrate that the proposed method can realize relatively accurate estimation of the friction with variations in speed and road gradient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".