Analysis of actuator faults in a cooperative team consensus of unmanned systems
Bibliographic record
Abstract
This work presents results on performance analysis of a team of agents in the presence of team members faults for three types of faults, i.e. loss of effectiveness, float, and lock-in-place faults. The team goal is to accomplish a cohesive motion in a modified leader-follower architecture using a semi-decentralized optimal control. This controller that is recently proposed by the authors is designed based on minimization of individual cost functions using local information. It is shown that a loss of effectiveness (LOE) fault does not deteriorate the stability or the consensus seeking goal of the team and 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 does not maintain its consensus anymore but stability of the team can be guaranteed. Moreover, the leader and the healthy followers adapt themselves to the followers changes when a float fault occurs in one of the agents. Finally, the behavior of the team in the presence of a lock-in-place (LIP) fault is also discussed. Simulation results are provided to demonstrate the performance of the team in the presence of the above faults.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".