Team Consensus for a Network of Unmanned Vehicles in Presence of Actuator Faults
Bibliographic record
Abstract
In this paper, performance analysis of a team of unmanned vehicles (agents) that are subject to actuator faults is investigated. The team goal is to accomplish a cohesive motion in a modified leader-follower architecture by using a semi-decentralized optimal control strategy. The controller, which is recently proposed by the authors, is designed based on minimization of individual cost functions by using the available information from the neighboring sets. It is shown that a loss of effectiveness (LOE) fault in an actuator does not deteriorate the stability nor the consensus seeking goal of the team. This fault would only result in a different transient behavior, e.g., a change in the agent's convergence rate, without a change in the consensus value. On the other hand, if the fault in one or more of the agents is of the float type, either in the leader or the followers, the team could not maintain its consensus any longer, however the stability of the team can still be guaranteed. Moreover, the leader and the healthy followers adapt themselves to the follower's change when a float fault occurs in one of the agents. Finally, the behavior of the team in presence of the lock-in-place (LIP) actuator fault is also investigated. Simulation results are provided to demonstrate the performance of the team subject to the above three actuator fault scenarios.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".