Distributed LQR Consensus Control for Heterogeneous Multiagent Systems: Theory and Experiments
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Bibliographic record
Abstract
Controlling heterogeneous multiagent systems (MASs) to cooperatively accomplish tasks is currently an emerging topic in the application-oriented research of robotics. This paper investigates the consensus problem of a MAS consisting of quadrotors and two-wheeled mobile robots (2WMRs). Directed and switching interaction topologies over the network are considered. We propose a distributed linear quadratic regulation (LQR) consensus protocol for the quadrotors and design an LQR-based Rotate&Run Consensus Scheme for the 2WMRs to update the states. We use the algebraic graph theory and stochastic matrix analysis to conduct the convergence analysis of consensus. The underactuation characteristic of the 2WMR dynamics is considered in the controller design. The effectiveness of the control methods is verified by simulations and experiments.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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