Finite-Time Multi-Lane Fusion Control for 2-D Plane Vehicle Platoon With FDI Attacks
Bibliographic record
Abstract
This paper proposes a finite-time prescribed performance control method to ensure effective two-dimensional (2-D) plane vehicle platoon multi-lane fusion and maintain platoon performance in the presence of unknown dynamic uncertainties and false data injection (FDI) attacks. Firstly, unknown dynamic uncertainties of the vehicle are approximated using radial basis function neural networks (RBFNNs). Building on this neural network approximation under FDI attacks, a novel state observer is developed to estimate the vehicle’s state, restore the communication protocol when the communication link is attacked, and address the complex coupling issues between vehicle states. Furthermore, finite-time prescribed performance control inputs are designed based on the constructed sliding surfaces to ensure practical finite-time stability of the 2-D plane vehicle platoon. This method facilitates vehicle multi-lane fusion within a finite time while guaranteeing platoon performance and preventing collisions. Finally, numerical simulations and comparative analyses are presented to demonstrate the effectiveness and superiority of the proposed control strategy, involving one leader vehicle and six followers.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".