Improving Performance for Multi-Agent Systems using Fuzzy-Logic Tuning and Mixed Feedback Controller
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Bibliographic record
Abstract
In this paper, an adaptive mixed feedback controller using fuzzy logic control (FLC) is proposed to improve the performance of the synchronization of a group of leader-follower agents with unknown time-varying communication delays. With the aim to improve the overall system performance while ensuring the stability under delays, Lyapunov-based methods and linear matrix inequality (LMI) techniques are applied to design a distributed control policy that uses agent state information with and without estimated self-delays. FLC is applied to online tune the control gains and weight of the self-delayed state in the controller as a nonlinear function of the total consensus error. Numerical simulations of a leader-follower group of five and seven DC motors are carried out to demonstrate the effectiveness and improvement in overall performance of the proposed controller.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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