Dynamic Modelling and Stability Controller Development for Articulated Steer Vehicles
Bibliographic record
Abstract
In this study, various stability control systems are developed to remove the lateral instability of a conventional articulated steer vehicle (ASV) during the oscillatory yaw motion or “snaking mode”. First, to identify the nature of the instability, some analyses are performed using several simplified models. These investigations are mainly focused on analyzing the effects of forward speed and of two main subsystems of the vehicle, the steering system and tires, on the stability. The basic insights into the stability behavior of the vehicle obtained from the stability analyses of the simplified models are verified by conducting some simulations with a virtual prototype of the vehicle in ADAMS. To determine the most critical operating condition with regard to the lateral stability and to identify the effects of vehicle parameters on the stability, various studies are performed by introducing some modifications to the simplified models. Based on these studies, the disturbed straight-line on-highway motion with constant forward speed is recognized as the most critical driving condition. Also, the examinations show that when the vehicle is traveling with differentials locked, the vehicle is less prone to the instability. The examinations show that when the vehicle is carrying a rear-mounted load having interaction with ground, the instability may happen if the vehicle moves on a relatively good off-road surface. Again, the results gained from the analyses related to the effects of the vehicle parameters and operating conditions on the stability are verified using simulations in ADAMS by making some changes in the virtual prototype for any case. To stabilize the vehicle during its most critical driving condition, some studies are directed to indicate the shortcomings of passive methods. Alternative solutions, including design of different types of stability control systems, are proposed to generate a stabilizing yaw moment. The proposed solutions include an active steering system with a classical controller, an active torque vectoring device with a robust full state feedback controller, and a differential braking system with a robust variable structure controller. The robust controllers are designed by using simplified models, which are also used to evaluate the ability to deal with the uncertainties of the vehicle parameters and its variable operating conditions. These controllers are also incorporated into the virtual prototype, and their capabilities to stabilize the vehicle in different operating conditions and while traveling on different surfaces during the snaking mode are shown.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".