Experimental verification of nonlinear sliding mode observer for accurate estimation of vehicle sideslip angle and lateral tire forces
Bibliographic record
Abstract
This paper investigates the estimation of the sideslip angle and lateral tire-road forces for a class of nonlinear road vehicles. A Second-Order Sliding Mode Observer (SOSMO) is developed as an extended-state observer to simultaneously estimate lateral tire forces along with other measurable variables. The vehicle’s sideslip angle is then estimated independently using two distinct methods: dynamical equations and an inverse model-based estimation approach. The latter method introduces an innovative tire model that incorporates nonlinear tire-road friction characteristics, effectively simulating lateral force behavior. Comparative simulations and experimental analyses across two practical scenarios demonstrate that the proposed strategies, which do not require detailed tire-road interaction modeling, outperform conventional estimators such as the Extended Kalman Filter (EKF) and the State Dependent Riccati Equation Filter (SDREF). The experimental results particularly highlight the superior accuracy and efficiency of the developed SOSMO-based estimation strategies in providing estimates of the sideslip angle and lateral forces, offering reliable and cost-effective solutions compared to traditional methods.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".