Robust and resilient MPC for path following of ASV against DoSattacks
Bibliographic record
Abstract
In recent years, autonomous surface vehicles (ASVs) control has gained increasing research attention since ASVs have broad applications, such as environmental protection, mapping, rescuing, and unmanned shipping, etc., where the ASV is usually assigned to travel in a specified sea area or to a pre-set point offshore. However, most of the ASVs have limited computational and data log capability. To address these limitations, many researchers design a networked control system for the ASV path-following problem. This networked framework usually consists of an ASV and a ground station: The ASV sends the sensor measurements to the ground station through the wireless network, and then the ground station calculates the control inputs based on the received information and sends the commands back to the ASV. However, this kind of networked structure makes the channel between the controller in the ground station and the ASV fragile and vulnerable to all kinds of cyber attacks, such as denial-of-service (DoS) attacks, false data injection (FDI) attacks, replay attacks, etc. Malicious attackers targeting on interfering with the channel between the ground station and ASV aim at preventing the ASV from achieving the desired goals, causing financial loss and security problems. At this point, we aim at proposing a robust and resilient model predictive control (MPC) framework to tackle the DoS attacks occurring on the ground station to the ASV channel and the external disturbances caused by the environments. Specifically, a packet transmission strategy is utilized to compensate for the lack of control signals induced by DoS attacks. At each sampling instant, the controller generates a lengthened control input sequence and transmits it to the buffer in the ASV. By following this approach, the ASV is able to utilize the control signal saved in the buffer when the channel is attacked. Furthermore, the MPC algorithm is designed with a robustness constraint to deal with external disturbances. The robustness constraint is constructed to confine the state of the nominal system in a tighter and tighter range with the time instant increasing to counter the effect caused by the external disturbances. In addition, the recursive feasibility and closed-loop stability will be theoretically analyzed. Finally, the effectiveness of the control framework will be verified through simulation results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".