Modelling and experimental validation of an adaptive interconnected suspension with adjustable roll stiffness (AIS-ARS)
Bibliographic record
Abstract
Vehicle suspensions play a critical role in improving vehicle stability and ride quality, especially in heavy vehicles that usually have high centre-of-gravity and carry tons of cargo. Interconnected suspension systems, in which the suspension struts are connected through hydraulic hoses and flow control devices, have great potential to enhance vehicle handling stability and attenuate vibrations. This study introduces an adaptive interconnected suspension with adjustable roll stiffness (AIS-ARS) to address the shortcomings of existing designs. It not only eliminates the conventional anti-roll bars but also enables adjustment of roll stiffness depending on the driving and vehicle load conditions. The AIS-ARS system uses a unique design that avoids using expensive electro-proportional flow control valves, which has been a significant barrier to widespread implementation. Instead, the system utilises a cost-effective control strategy that uses only two solenoid valves to achieve adaptive damping. The mathematical modelling of the AIS-ARS is also straightforward, requiring less computational power and making it more practical for real-time implementations. Overall, the AIS-ARS system represents a significant advancement in the design of interconnected suspension systems, offering a more cost-effective, practical, and versatile solution. The above features are validated through laboratory experiments and co-simulation between MATLAB/Simulink and ADAMS/Car.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".