A novel adaptive control approach for path tracking control of autonomous vehicles subject to uncertain dynamics
Bibliographic record
Abstract
Autonomous ground vehicles are constantly exposed to matched/mismatched uncertainties and disturbances and different operating conditions. Consequently, robustness to resist the undesirable effect of changes in the nominal parameters of the vehicle is a significant provision for satisfactory path-tracking control of these vehicles. The accomplishment of lateral path-tracking control is an essential task expectable from autonomous ground vehicles, particularly during critical maneuvers, abrupt cornering, and lane changes at high speeds. This paper presents a new control approach based on immersion and invariance control theorem. The asymptotic stability of the proposed method is ensured and the adaptation laws for the parameters are derived based on the I&I stability theorem. The effectiveness of the proposed control method is confirmed for autonomous ground vehicles systems while making a double-lane-change at various forward speeds. The robustness of the proposed control method is evaluated under parametric uncertainties related to the autonomous ground vehicle and different road conditions. The obtained results suggest that the proposed control method holds the capacity to be applied effectively to the path-tracking task of autonomous ground vehicles under a broad range of operating conditions, parametric uncertainness, and external disturbances.
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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 | 0.001 |
| 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".