Sliding Mode Control of Laterally Interconnected Air Suspensions
Bibliographic record
Abstract
This paper presents a control method based the lateral interconnected air suspension system, in order to improve the road handling of vehicles. A seven-DOF (Degree of freedom) full-vehicle model has been developed, which considers the features of the interconnected air suspension system, for example, the modeling of the interconnected pipelines and valves by considering the throttling and hysteresis effects. On the basis of the well-developed model, a sliding mode controller has been designed, with a focus on constraining and minimizing the roll motion of the sprung mass caused by the road excitations or lateral acceleration of the vehicle. Moreover, reasonable road excitations have been generated for the simulation based on the coherence of right and left parts of the road. Afterwards, different simulations have been done by applying both bumpy and random road excitations with different levels of roughness and varying vehicle lateral accelerations. The simulation results indicate that the interconnected air suspension without control can improve the ride comfort, but worsen the road handling performance in many cases. However, by applying the proposed sliding mode controller, the road handling of the sprung mass can be improved by 20% to 85% compared with the interconnected or non-interconnected mode at a little cost of comfort.
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 | 0.000 |
| 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) | 0.001 | 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".