A Blending Based Multiple Model Reference Adaptive Approach to Lateral Vehicle Motion Control
Bibliographic record
Abstract
This paper studies reference tracking control of uncertain lateral vehicle dynamics, using a blending based multiple-model reference adaptive control (MMRAC) approach to overcome the parametric uncertainties and time-variations, including those in the tire force capacities and cornering stiffness. The lateral vehicle dynamics model under consideration is multiple-input, multiple-output, linear, and parameter varying. The design will assume a time-invariant system, such that all uncertain parameter variations lie inside of a known, compact, and convex set. The proposed MMRAC law guarantees perfect tracking of the desired state values generated by a linear reference model representing ideal driving conditions, and the system parameter estimates asymptotically converge to the unknown true values. We present simulations to show the stability and effectiveness of the proposed MMRAC scheme, even in the presence of slow time variations, as well as a performance comparison with existing lateral vehicle motion controllers.
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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".