Energy- and Cost-Efficient Transmission Strategy in Networked UAV Control System with ADP Trajectory Tracking Control
Bibliographic record
Abstract
In this paper, we consider a networked control system (NCS) with bidirectional network-induced delay, in which the control center needs to control the remote unmanned aerial vehicle (UAV) to complete the trajectory tracking task. The sensor of the remote controlled UAV adopts the event-triggered mechanism, and the control center uses the adaptive dynamic programming (ADP) method to generate control actions. The application of ADP method to NCS brings new transmission options, that is, transmitting control action or neural network (NN) model. There exists a fundamental tradeoff between different transmission options with different transmission energy consumption and tracking cost, which still receives little attention in the NCS design. To fill this gap, we propose a cost-based transmission strategy that can balance the average energy consumption and the average tracking cost. By deliberately making decisions on whether to transmit the control action or the NN model, the weighted sum of the average energy consumption and the tracking cost is minimized. Simulation results show that compared with the benchmark strategies, the proposed strategy can achieve a better compromise in the long-term average energy consumption and long-term average tracking cost, and can obtain better performance in a specific weight range.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".