Adaptive Dynamic Event-Based Robust Control for Multiple Networked Euler–Lagrange Systems
Bibliographic record
Abstract
This article develops an event-based adaptive robust control scheme for multiple networked Euler–Lagrange systems with a dynamic leader, addressing some key challenges such as parameter uncertainties, unknown perturbations, inherent nonlinearities, and limited resources, for the practical applications of networked robotics and autonomous systems. To reduce the communication network burden and the computational resources consumption, an adaptive dynamic triggering strategy is developed. In addition, to estimate the inaccurate states, a nested adaptive sliding-mode estimator is proposed. Then, a fully distributed adaptive dynamic event-based time-varying sliding-mode control strategy is developed based on the designed triggering scheme and estimator, without requiring any global information. This strategy reduces the effect of large initial errors on the varying gain during adaptation, and compensates for the influences of inherent nonlinearities, unknown external perturbations, and parameter uncertainties, making it feasible for practical implementation. Moreover, Lyapunov stability theory is used to guarantee the asymptotic convergence of the closed-loop networked systems. Finally, hardware experiments are conducted using multiple quadrotors to validate the effectiveness of the proposed control scheme in multiagent coordination tasks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".