Adaptive fixed-time output feedback formation control for nonstrict-feedback nonlinear multi-agent systems
Bibliographic record
Abstract
In this paper, an adaptive fixed-time output feedback formation control problem is investigated for nonlinear multi-agent systems with a nonstrict-feedback structure. In the controller design procedure, the neural network state observer is designed to estimate the unmeasurable state variables. Dynamic surface control (DSC) technique is applied to avoid the repeated differentiation for the virtual control signals. The dynamic surface compensation signals can realise the practical fixed-time bounded. Utilising the classified discussion method, the difficulty of controller design caused by the existence of observer error term is addressed. The technique of transformation of the index set is employed to cope with the related variables of the neighbour states, which simplifies the controller design. Under the presented control mechanism, all closed-loop signals remain bound for a fixed period of time, the formation control performance target between all followers and leader can be achieved. And the formation errors and state observers errors are both bounded such that can converge to a little domain around zero. Simulation results are provided to test the availability of the presented strategy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".