Robust Formation Control of Nonlinear Agents with Distance Constraints
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper studies robustness of the distance-based formation control problem for a set of nonlinear agents with additive uncertainties. Directed graph theory is used to model the desired formation topology, and the task of controlling an edge is given to only one of its incident agents. We proposed a distributed, robust control method for the distance-based formation control of uncertain nonlinear agents. We designed a distributed optimal controller for the nominal system. We then modified the nominal controller for the uncertain system by adding an integral sliding mode control (ISMC). A rigorous Lyapunov stability analysis is carried out to show the asymptotic convergence of each agent to its desired formation. Then, using the stability theory of cascaded interconnected systems, and the concept of mathematical induction, the stability of the overall formation is proven. Simulations results illustrate the effectiveness of the proposed method in two- and three-dimensional spaces.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it