Optimal performance of a modified leader-follower cooperative team with partial availability of the leader command and agents actuator faults
Bibliographic record
Abstract
The objective of this work is performance analysis for a cooperative team of agents in presence of team members faults. The team goal is to accomplish a cohesive motion in a modified leader-follower architecture using a semi-decentralized optimal control introduced previously by the authors. This controller is designed based on minimization of individual cost functions over a finite horizon using local information. The desired output (command) is assumed to be available to only the leader while the followers should follow the leader using information exchanges existing among themselves and the leader(s) through a predefined topology. It is shown that in case of faults in one or more agents, either in the leader or followers, the team maintains its stability. Also, the final steady state value to which the team would converge in these cases are obtained. It is shown that the modified structure used for the team enables the leader to adapt itself to followers faults. For instance, in case of a follower speed reduction due to an actuator fault, the leader would decrease its speed to adapt itself to the failed agent. Finally, simulation results are provided to demonstrate achievement of the prespecified team fault tolerant cooperative requirements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".