Automated steering controller design for vehicle lane keeping combining linear active disturbance rejection control and quantitative feedback theory
Bibliographic record
Abstract
In this article, a new automated steering control method is presented for vehicle lane keeping. This method is a combination between the linear active disturbance rejection control and the quantitative feedback theory. The structure of the steering controller is first determined based on the linear active disturbance rejection control, then the controller is tuned in the framework of the quantitative feedback theory to meet the prescribed design specifications on sensitivity and closed-loop stability. The parameter uncertainties of the vehicle system are considered at the tuning stage. The proposed steering controller is simulated and tested on a scale vehicle. Both the simulation and experimental results demonstrate that the scale vehicle controlled by the proposed controller is able to perform the lane keeping. In the experiments, the lateral offset between the scale vehicle and the road centerline is regulated within the acceptable ranges of ±0.03 m during straight lane keeping and ±0.15 m during curved lane keeping. The proposed controller is easy to be implemented and is simple without requiring complex calculations and measurements of vehicle states. Simulations also show that the control method can be implemented on a full-scale vehicle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".