Novel Transmissibility Shaping Control for Regenerative Vehicle Suspension Systems
Bibliographic record
Abstract
This research proposes a novel transmissibility shaping control (T-shaping Control) method and explores its potential performance benefits for active vehicle suspension systems with energy-regeneration [1]. The proposed model-free T-shaping control integrates a range of sub-strategies based on the frequency information extracted from measured dynamic signals. Each strategy is designed to function dominantly in a certain frequency range to achieve a desirable (or optimal) transmissibility of vehicle responses for enhanced vehicle dynamic performance and safety. Different sub-strategies employed for different frequency ranges consist of stiffness control, skyhook control, groundhook control, and variable damping. In order to demonstrate the effectiveness of this proposed control method, a novel tunable compressible fluid strut (CFS) integrating with digital displacement pump motor (DDPM) is used to form an energy-regenerative controllable vehicle suspension system [2–4]. Two vehicle models, including quarter-car and full-vehicle models, are employed to investigate the dynamic performance of a road vehicle with the proposed T-shaping control and novel regenerative suspension system. The results demonstrate the effectiveness and considerable performance enhancements of the proposed novel T-shaping control applied to the novel CFS suspension system in a very energy-efficient manner.
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 | 0.001 | 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".