Nonlinear Robust Control of Vehicle Lateral Dynamics Considering Driver’s Dynamics
Bibliographic record
Abstract
Guaranteeing stability of a vehicle without considering the driver in the control loop is difficult. In this paper, a driver-in-the-loop control strategy is proposed to improve the lateral vehicle behavior and extend the stability margin. The driver is modeled as a delayed linear controller with the aim of tracking the desired path. The main aim of the controller design is to track the desired yaw rate of the vehicle considering the driver effects. To make an implementable approach, it is assumed that the desired road information and the exact values of longitudinal and lateral forces are not available for the control level and the controller treats them as bounded uncertainties. The nonlinear damping technique is adopted to stabilize the yaw rate error. For two different robust designs, we have shown that the yaw rate error will confine inside a certain neighborhood even in the presence of uncertainty. The size of this neighborhood is directly proportionate to the gain of the robust control terms and the driver characteristics. A standard harsh double lane change scenario is simulated as the desired path for the driver. The results demonstrate that the design process improves the overall behavior of the driver-vehicle system in the presence of bounded uncertainties.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".