A comparative study of control algorithms for active trailer steering systems of articulated heavy vehicles
Bibliographic record
Abstract
This paper presents a benchmark investigation on control algorithms for active trailer steering (ATS) systems of articulated heavy vehicles (AHVs). Two control algorithms are designed for the ATS systems of AHVs, which are derived using the Fuzzy Logic Control (FLC) and Linear Quadratic Regulator (LQR) techniques, respectively. The two algorithms are evaluated through dynamic simulations of an AHV with ATS systems. The AHV with a tractor/semi-trailer configuration is modeled using a multibody software package, TruckSim, and the controllers are designed in Simulink/Matlab. With the interface between the two packages, the controllers and the vehicle model are integrated and the dynamic simulations can be conducted. Under the specified test maneuvers, the directional performance of the ATS systems based on the two algorithms are compared. The simulation results show that compared with the baseline design, both the FLC and LQR controllers can improve the maneuverability and stability of the AHV. The performance comparison also indicates that the controller based on the LQR technique outperforms that derived from the FLC method. However, the former achieves better performance at the expense of higher energy consumption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".