Adaptive Fractional-Order Fault-Tolerant Coordinated Tracking Control of Heterogeneous Multiagent Systems Against Multiple Faults Under Deception Attacks
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Bibliographic record
Abstract
This article addresses the issue of the adaptive fractional-order fault-tolerant coordinated tracking control (FO-FTCTC) for multiple unmanned aerial vehicles and unmanned ground vehicles with fixed-time prescribed performance subjected to actuator and sensor faults under deception attacks. Deception attacks disrupt the sensor network, making the output and state unavailable. To achieve the tracking control of the system, the coordinate transformation method is developed, in which the attack gains are considered and the compromised states are utilized to design a control scheme. Then, the fixed-time prescribed performance function (PPF) is illustrated to transform the coordinated tracking errors (CTEs) into another error variable so that the unconventional errors are limited to the prescribed range. Next, the sliding-mode surface is built by utilizing the errors and fractional calculus. In addition, the radial basis function neural network is utilized to deal with the unknown term. Based on the error, the adaptive FO-FTCTC scheme by utilizing the radial basis function neural network (RBFNN), fractional calculus, and fixed-time PPF with prescribed performance can be achieved, which can strengthen the system performance. Based on the Lyapunov function approach, all vehicles can coordinately track their desired references and CTEs can be bounded within the prescribed boundary. Finally, simulation studies are provided to verify the validity of the developed FO-FTCTC scheme.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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