A Secure Adaptive Resilient Neural Network-Based Control of Heterogeneous Connected Automated Vehicles Subject to Cyber Attacks
Bibliographic record
Abstract
In the realm of intelligent transportation systems (ITSs), safeguarding the resilience of connected automated vehicles (CAVs) with vulnerable interactions is imperative, particularly amidst the rapid spread of cyber-attack effects within the system. This paper introduces a pioneering Neural Network-based Cooperative Adaptive Resilient Control (NNCARC) approach that seamlessly integrates adaptive neural networks and resilient control mechanisms to counteract the impacts of nonlinearity, cyber-attacks, and external disturbances. The methodology commences with the development of an adaptive neural network to precisely estimate system nonlinearity, followed by the proposal of a cooperative adaptive resilient control strategy leveraging the Lyapunov theorem for stability analysis and adaptive laws. To the authors’ knowledge, this is the first time that a NNCARC is proposed which ensures all vehicles within a platoon, with any type of network topology, adhere safely to the leader's time-varying profile, without necessitating additional controller switching algorithms in the event of a cyber-attack. By eliminating restrictive assumptions like the Lipschitz condition on nonlinear components, the proposed methodology enhances its versatility and robustness. Theoretical analyses validate system stability and objective achievement, while simulation studies across diverse network topologies, cyber-attack scenarios, and external disturbances substantiate the efficacy of the approach in controlling CAVs within a platoon. This paper constitutes a significant advancement in resilient control methodologies for CAVs, offering a comprehensive solution to mitigate cyber-attack and disturbance effects while ensuring system stability and performance.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".