Robust Learning-Based Gain-Scheduled Path Following Controller Design for Autonomous Ground Vehicles
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Bibliographic record
Abstract
In this paper, a robust gain-scheduled path following controller for automated vehicles based on learning methods is presented. Two major challenges are overcome:1) Varying longitudinal velocity, uncertain cornering stiffness, and unmodelled uncertainties make dynamic-model-based controller design work complex. 2) Driving scenario changes deteriorate path following controller performance. An effective learning method, online updating least squares-support vector machine (LS-SVM) model is adopted for vehicle path following system considering varying velocity and cornering stiffness in this paper. Then the updating LS-SVM model is transformed into linear-parameter-varying (LPV) model with disturbance. The robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> controller design method is novelly employed to design path following controller for updating LS-SVM model. By this method a gain-scheduled output-feedback controller is designed. To improve transient performance, the poles of closed-loop system are assigned to desired regions. Simulation results using a high-fidelity and full-car model from CarSim have verified the effectiveness of the proposed control strategy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 it