An Improved Backstepping Control Method for Trajectory Tracking of Autonomous Vehicles
Bibliographic record
Abstract
This paper proposes an improved backstepping-based trajectory tracking control strategy for autonomous vehicles (AVs). Unlike traditional backstepping control, the proposed method significantly reduces computational complexity by nicely introducing a virtual feedback variable related to the longitudinal error. Instead of requiring multiple intermediate state variables, the new way of introducing virtual variable has a simple structure and results in easy derivatives. By doing so, the designed back-stepping smoothly fits the recursive integration framework and effectively improves computational efficiency. It is further theoretically proved that the proposed controller can make tracking system approximately globally asymptotically stable even under the influence of input saturation. To verify the effectiveness and practicality of the proposed strategy, both MATLAB/Simulink simulation and QCar experiment were conducted by comparing with two typical control approaches. The comparative results have shown that the proposed method ensures lateral, longitudinal, and yaw angle errors to rapidly converge to zero under input constraints, reflecting high feasibility and stability of tracking curvy trajectories.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".