A coordinated control system for truck cabin suspension based on model predictive control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Truck drivers are constantly exposed to undesirable vibrations caused by uneven road surfaces, especially in long-distance transportations. Nowadays, a truck is usually equipped with two suspension systems, namely the primary and the cabin suspensions. The cabin suspension acts as a vibration isolation system between truck chassis and driver cabin. Different techniques have been proposed to eliminate undesired vibrations transmitted to drivers, among which the semi-active cabin suspension is one of the most effective approaches. In this study, a coordinated semi-active suspension system for improving ride comfort in vertical, roll, and pitch directions is introduced. To solve this constrained multi-objective optimisation problem, the model predictive control (MPC) is utilised. Meanwhile, the Skyhook control is considered as a benchmark. These two controllers are examined through the cosimulation between ADAMS/Car and MATLAB/Simulink. The results demonstrate that MPC has significant advantages over Skyhook in terms of optimising cabin dynamics and improving ride comfort.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it