Research on Steering and Suspension–Integrated Control Based on Robust MPC Considering Road Vibration Prediction
Bibliographic record
Abstract
The steering and suspension systems, as key actuators of intelligent vehicles, have a significant impact on vehicle handling stability and ride comfort. However, in view of the coupling between them, it is difficult to balance the above two aspects of performance when controlled separately. To improve the comprehensive control effect of the automobile chassis system, this paper presents an integrated control method for active steering and active suspension system (ASS) based on robust model predictive controller (MPC) considering road vibration prediction. The designed controller considers the motion of the vehicle in yaw, roll, pitch, and vertical directions. Based on the established vehicle dynamics model, road surface model, and driver model, the integrated system control framework is constructed. Then, the proposed robust MPC (R‐MPC) with road vibration prediction is testified through simulation. The results show that the R‐MPC has better anti‐interference ability than the MPC method under road excitation or slippery road condition, and the comprehensive performance of vehicle handling stability and ride comfort can be improved through road vibration prediction.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".