Adaptive Time-Delay Control for Cooperative Platooning Considering Slip
Bibliographic record
Abstract
This article introduces a slip-aware networked vehicle model and proposes an adaptive time-delay control framework for connected autonomous driving systems’ cooperative adaptive cruise control and safety of the intended functionality. In order to improve the vehicular network safety by conveying the amount of longitudinal slip ratio along with the vehicle kinematic states, the innovative slip-aware model makes use of an auxiliary state variable representation that includes wheel slips. The proposed framework enables the formation of platoons consisting of vehicles from multiple manufacturers with similar dynamics, with each vehicle requiring state measurements and longitudinal slip information only from its preceding vehicle. To ensure robustness against external disturbances and model uncertainties, an artificial time-delayed control-based technique is implemented to control the entire networked vehicle system. In order to achieve disturbance rejection along the string of vehicles, control protocols have to be designed to ensure string stability of the whole vehicle platoon. The robust law is augmented with a dual-rate adaption law in order to tackle the overestimation and underestimation problem of switching gain. Subsequently, Lyapunov stability analysis is conducted to show that the inter-vehicular states are steered within a small region in the neighborhood of the origin, under the proposed adaptive-robust control scheme. Numerical simulations are also carried out to validate the robustness of the developed distributed control framework.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".