Event-Triggered Adaptive Optimal Control of Vehicular Platoons via Fuzzy ADP With Prescribed Performance
Bibliographic record
Abstract
The control problem for connected vehicular platoons requires balancing control optimality, saving computational and communication resources, and ensuring security. In this paper, a fuzzy prescribed performance adaptive optimal control strategy is developed for platoon system, and a distributed event-triggered (ET) mechanism is introduced. The main contributions include: 1) A prescribed performance adaptive dynamic programming (ADP) control architecture under distributed event-triggering is developed. The designed control method not only ensures the safety distance requirements of the platoon, but also significantly reduces the computing and communication costs, while guaranteeing the control optimality under the above objectives; 2) The stability proof of the platoon system considering the above complex control objectives is completed. Stable convergence of fuzzy logic system (FLS) is ensured by designing an experience replay-based critic update rule; 3) Considering the practicability in real working conditions, the cost function of the ADP controller robust to actuator saturation and disturbance is designed. Compared with existing methods, our approach achieves optimal control, robustness, and enhanced communication efficiency. Finally, the effectiveness and applicability of the controller are verified by simulations.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".