A Robust Path Tracking Control Method for Intelligent Vehicle
Bibliographic record
Abstract
This paper presents a strong robust path tracking control method which is based on sliding mode control and active disturbance rejection control. Firstly, by constructing a desired yaw angle function, which can guarantee that the deviations of the vehicle actual lateral displacement from the desired path converges to zero when the yaw angle of the vehicle approaches the desired yaw angle, so that the complex path tracking control problem can be transformed into easy to implement yaw angle tracking control problem. Then, a robust vehicle yaw angle tracking controller is constructed. The controller consists of two parts: the extended state observer and the nonlinear error feedback control law. The extended state observer is used to estimate the unmodeled dynamics and unknown external perturbations of the system in real time. The nonlinear error feedback control law is designed by combining the nonsingular terminal sliding mode and exponential approximation law to compensate the system total disturbance and achieve accurate yaw angle tracking control. The improved control system has better control quality and response characteristics. In order to verify the effectiveness of the proposed path tracking control method, using CarSim and Simulink to simulate the typical driving conditions, the simulation results show that the controller designed in this paper can ensure that the intelligent vehicle track the reference path quickly and precisely and has strong robustness.
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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) | 0.002 | 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".