Active Suspension with PID Control for Enhanced Rollover Stability in Liquid Tank Trucks
Bibliographic record
Abstract
The enhancement of rollover stability in vehicles, particularly in liquid tank trucks, is imperative for reducing the risk of vehicular rollovers and consequent threats to human life and property.The dynamic behavior of the liquid within the tank during maneuvers such as turning or lane-changing plays a significant role in the vehicle's stability.It has been demonstrated that the implementation of a Proportional Integral Derivative (PID) controller within the stabilizer bar of an active suspension system can substantially mitigate the vehicle's propensity to roll.This mitigation is quantitatively evidenced by reductions in the lateral load transfer ratio (LTR) and the roll angle (TRA) of the suspension system.Specifically, during steady turning maneuvers, a decrease of 5.6% in LTR and 12.9% in TRA was observed for liquid levels deemed most hazardous at heights of 1.2m and 1.6m, respectively.Similarly, during lane-changing maneuvers, LTR was reduced by 6.2%, and TRA was decreased by 9.1% at critical liquid levels of 0.8m and 1.6m.These analytical outcomes were derived through the application of the Lagrange method and the quasi-static method for establishing the fluid vibration equation within the tank, supplemented by D'Alembert's principle in constructing a four Degrees of Freedom (DOFs) roll model for the differential equation system of vehicle motion.This research underpins the foundational principles for the design, manufacture, and enhancement of suspension systems, and elucidates the direction for future investigations into tank trucks transporting various liquid types.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".