Fault tolerant cooperative control of multiple UAVs-UGVs under actuator faults
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".