Model Reference Adaptive Control for Active Trailer Steering of Articulated Heavy Vehicles
Bibliographic record
Abstract
This paper proposes a model reference adaptive control (MRAC) strategy for active trailer steering (ATS) in order to improve the lateral stability of articulated heavy vehicles (AHVs). Optimal controllers based on the Linear Quadratic Regulator (LQR) technique have been explored to enhance the lateral stability of AHVs; these controllers are designed under the assumption that the vehicle model parameters and operating conditions are given and they remain as constants. However, in reality, the vehicle system parameters and operating conditions may vary. To address the variable payloads of trailer(s), the controller based on MRAC technique is adopted. A three degrees of freedom (DOF) linear yaw-plane tractor-semitrailer model is generated to design the control law. The reference model is also developed using the linear yaw-plane model with the LQR technique. The effectiveness of the MRAC controller is demonstrated using numerical simulations under an emulated single lane-change maneuver.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".