A Predictive Control Strategy for Remotely Maneuvered Wheeled Mobile Robots Enabling Setpoint Attack Detection
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Bibliographic record
Abstract
In this article, we consider remotely maneuvered differential-drive robots whose tracking controller is implemented on-board while the desired reference signal is generated by a remote control center and transmitted using a wireless communication channel potentially prone to cyber-attacks. Here, we develop a novel networked control architecture that allows the robot to track a given reference signal while enabling, on the robot's side, the detection of false data injections on the setpoint (reference) signal. The proposed solution takes advantage of a feedback linearized model of the vehicle kinematic model, a detector unit, and the coupled actions of two distributed predictive command governor modules installed at the two ends of the communication channel. We show that the resulting architecture guarantees constraints fulfillment and the absence of stealthy setpoint attacks. Laboratory experiments on a Khepera IV robot testify to the effectiveness of the proposed solution.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it