Safe Control of Multiagent Systems via Low-Complexity Control Barrier Functions
Bibliographic record
Abstract
In its standard formulation, the Control Barrier Function (CBF) method scales poorly as the order of the system dynamics increases. On the one hand, the need of recursively extending the control with higher-order terms along the backstepping procedure inevitably results in dynamically increasing complexity. On the other hand, the lack of an explicit form of the CBF leads to implicit formulations of the safety conditions, to be solved numerically via quadratic programming. These high complexity and poor scalability issues further amplify in multi-agent systems. This work proposes a low-complexity framework for safe control of higher-order multi-agent systems within the philosophy of funnel control. Different from the state of the art, safety is expressed in terms of a CBF that is explicitly constructed from funnel functions, thus providing a solution to the well-known problem of lack of systematic methods for constructing a CBF. Low complexity comes from the fact that the control does not involve complex dynamical extensions during the backstepping procedure. Notably, the proposed CBF is shown to automatically satisfy by design the safety conditions, without resorting to any quadratic programming. Lyapunov analysis shows that the design allows to consider individual terms for stability and safety, so as to accomplish these tasks in a seamless integrated fashion. Simulation studies and comparisons with the state of the art further demonstrate the method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".