Active control of a truck full–trailer's rearward amplification
Bibliographic record
Abstract
This paper presents the results of examining the influence of tyres' uncertainties (represented by the variation of the cornering stiffness) on the actively controlled rearward amplification (RWA) of six–axle truck/full–trailer. In this preliminary study, a four degrees of freedom yaw/plane model has been developed and used. The RWA is defined as the ratio of the peak lateral acceleration at the rearmost trailer's centre of gravity (cg) to that at the cg of the lead unit during lane–change manoeuvre. The vehicle under consideration is a 6–axle truck/full–trailer, which usually exhibits a high level of rearward amplification ratio leading to rollover during obstacle avoidance manoeuvres. In this study, the effect of the active control torque applied to the dolly is examined by using the optimal linear quadratic regulator (LQR) and sliding mode controller approaches. Unlike the LQR algorithm, the sliding model controller is designed to account for uncertainties (tyres cornering stiffness variation in this study). The control performance index criteria are determined for the vehicle based on an acceptable RWA target value. For active yaw control at the dolly cg, the optimal controller is found to be most sensitive to the dolly's tyres, cornering stiffness variations and least sensitive to steering axle from the RWA ratio point of view. It is also found that the controller was determined to be most sensitive to the steering axle parameter variations for the path following case. Simulation results indicate that the rearward amplification ratio can be reduced without significant change of the uncontrolled vehicle trajectory when active yaw torque is applied to the dolly. The sliding mode controller is found to be more effective in improving the dynamic performance in terms of reducing the rearward amplification during severe lane–change manoeuvres. This improves the vehicle roll stability, particularly when parameter uncertainties such as tyre cornering stiffness are present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".