Development of sliding mode observers for estimating sideslip angle and lateral forces in road vehicles
Bibliographic record
Abstract
This study presents a second-order sliding mode observer (SOSMO) framework developed to improve the estimation of lateral tire forces in vehicles. The framework incorporates two distinct approaches for approximating the sideslip angle: one based on dynamical equations and another employing an inverse model estimation technique with a new tire model. The suggested tire model captures the nonlinear characteristics of tire–road friction, enabling a more accurate representation of lateral force behavior. Comparative evaluations are conducted using a single-track vehicle model based on the Pacejka formula under two scenarios: the open-loop steering pad maneuver and the lane change maneuver. Simulation results demonstrate the superior performance of the proposed methods compared to two established observers, namely, the extended Kalman filter and the state-dependent Riccati equation (SDRE) filter, even in the absence of detailed tire–road interaction models. Notably, in a steady-state circular driving scenario, the second approach achieves a 99% smaller error compared to the first approach and a 99.38% smaller error relative to the SDRE filter. In a transient maneuver scenario, the second approach achieves a 10.71% smaller error than the first approach and a 99.63% smaller error compared to the SDRE filter. Robust studies under external disturbances further confirm the proposed methods’ precision and reliability in estimating sideslip angle and lateral tire forces, offering a cost-effective alternative to traditional tire–road interaction models.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".