Path-tracking control based on adaptive tube-based robust MPC for high-speed intelligent vehicles
Bibliographic record
Abstract
To improve path-tracking performance of high-speed intelligent vehicles, this paper proposes a path-tracking control design method based on an adaptive tube-based robust model predictive control (A-TRMPC) controller. Built upon a tube-based robust model predictive control (Tube-RMPC) controller with linear quadratic regulator (LQR) feedback control, the A-TRMPC is designed by incorporating a variable look-ahead preview distance inspired by human-driver driving and considering variable prediction and control horizon. The A-TRMPC exhibits the distinguished features: (1) adapting different operating conditions by varying the prediction horizon, control horizon, and the look-ahead distance; (2) tightening constraints by introducing a robust positive invariant set to improve the robustness of the controller against prediction vehicle model errors and external uncertainty disturbances. Considering the influence of prediction horizon, control horizon and look-ahead distance on the control effect, these parameters are treated as design variables, which are optimized offline using a quantum particle swarm optimization (QPSO) search algorithm with path-tracking accuracy and vehicle stability as design criteria. Finally, the effectiveness of the proposed path-tracking control design method is evaluated with a benchmark comparison using CarSim–Matlab/Simulink-based co-simulation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".