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 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".