Model-Reference Based Adaptive Control for Enhancing Lateral Stability of Car-Trailer Systems
Bibliographic record
Abstract
This paper presents a model reference adaptive control (MRAC) approach to enhance the lateral stability of car-trailer systems. To this end, a 3 degrees of freedom (DOF) linear yaw-plane car-trailer model was developed as a “reference model”. The yaw rate of leading and trailing units of the reference model were used as the target states to control and stabilize a virtual vehicle plant represented by a 5 DOF linear yaw-roll car-trailer model. A Lyapunov-based controller was designed to handle the lateral stability of the car-trailer dynamical system. The model parameters and operating conditions of the system were predefined while designing the controller. The effectiveness of the adaptive controller for improving the lateral stability of car-trailer systems was demonstrated under a simulated multiple cycle sine-wave steering input maneuver. It was observed that the lateral stability of car-trailer system was improved by controlling respective yaw rates of the car and the trailer, using model reference adaptive control approach in conjunction with Lyapunov stability criterion.
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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 | 0.000 |
| 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) | 0.001 | 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".