Fault tolerant cooperative control of multiple UAVs-UGVs under actuator faults
Why this work is in the frame
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Bibliographic record
Abstract
A fault tolerant cooperative control (FTCC) strategy for a team of an unmanned aerial vehicle (UAV) and unmanned ground vehicles (UGVs) in the presence of actuator faults are investigated in this paper. A combination of a linear model predictive control (MPC) and input-output feedback linearization is implemented on each UGV, while a combination of a sliding mode control and linear quadratic regulator (LQR) are applied to the UAV. When a severe actuator fault occurs in one of the robots, it becomes unable to complete its assigned task, and it has to get out from the formation mission. FTCC strategy is designed with the robots' tasks are re-assigned to the remaining healthy robots to complete the mission with graceful degradation. The FTCC problem is solved as an optimal assignment problem, while a Hungarian algorithm which applied to each robot will solve the assignment problem. Formation operation of the robot team is based on a leader-follower approach, and the control algorithm is implemented in a decentralized manner. Finally, simulation results are presented in order to demonstrate the performance of the team in both fault-free case and faulty case.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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