Vibration analysis and adaptive model predictive control of active suspension for vehicles equipped with non-pneumatic wheels
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an adaptive controller is proposed for an active suspension system to achieve optimal compromise performance for vehicles equipped with non-pneumatic wheels under different road conditions. Firstly, the effective vertical stiffness of the non-pneumatic wheel (NPW) was identified through the static force-deflection tests. Then, the effect of the variations in NPW stiffness and mass on the vibration responses was investigated using a quarter-vehicle model. In order to coordinate the ride comfort and handling performance of the vehicle for different road excitations, an adaptive controller was synthesized using the model predictive control (MPC) theory together with an H ∞ state observer. The control gains for different control objectives were determined using a genetic algorithm (GA). Simulations indicate that the proposed controller can adapt to different road excitations and effectively enhance the dynamic performance of the vehicle. Specifically, by applying adaptive control, the root-mean-square (RMS) value of sprung mass acceleration (SMA) and the dynamic wheel load (DWL) coefficient are reduced by 19.4% and −9.3% on Class B roads and 12.4% and 3.8% on Class C roads, respectively, which is superior to the modified skyhook control (19.4% and −11.8% on Class B roads, and 19.3% and −12.3% on Class C roads). The effectiveness of simulation results was subsequently verified through hardware-in-the-loop experiments.
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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 | 0.000 | 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 | 0.001 |
| 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 it