Vehicle Lateral Motion Control Based on Fading Sage–Husa Kalman Filter and Robust Model Predictive Control
Bibliographic record
Abstract
Vehicle lateral motion control is one of the critical issues in intelligent vehicle control. We design a vehicle lateral motion controller by combining the adaptive fading Sage–Husa Kalman filter (AFSH‐KF) with the robust model predictive algorithm to address the problem of vehicle lateral motion control. Due to the influence of process and measurement noise on the estimation results, the AFSH‐KF is employed to estimate the vehicle state parameters to improve the estimation accuracy and compared with the Kalman filter (KF). Simultaneously considering the influence of the uncertainties or perturbations appearing in the feedback loop (vehicle state parameters) on vehicle lateral motion control, a robust model predictive controller (RMPC) is designed for vehicle lateral motion. The performance of the designed controller is verified by co‐simulating with MATLAB/Simulink and CarSim in double‐lane, S‐shape, and Fishhook conditions. The results show that the AFSH‐KF can effectively estimate the states (yaw rate and sideslip angle) of the vehicle. Compared to the MPC controller, the RMPC controller significantly reduced the maximum and mean square error of the lateral deviation of the vehicle tracking target trajectory at different speeds.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".