Control Performance Aware Cooperative Transmission in Multiloop Wireless Control Systems for Industrial IoT Applications
Bibliographic record
Abstract
The wide application of Internet of Things (IoT) in industrial automation encourages the emergence of a new paradigm of industrial IoT systems, wireless control system (WCS), where the system and/or control information is delivered over wireless channels. In practical systems, WCSs would consist of multiple control-loops in general, the resource competition among which would seriously increase mutual interferences and transmission collisions, making it is difficult to provide the required transmission reliability for the control strategy. To address this issue, we design the control strategy together with the hybrid cooperative transmission scheme for multiloop WCSs in a proactive way. We first define the overall system cost function to explore the impacts of standard linear quadratic regulator control cost and wireless transmission reliability on the control performance. In order to further minimize the overall system cost while guaranteeing the control stability, we then propose a control performance aware cooperative transmission scheme, which is formulated as a constrained optimization problem. Decomposition method and heuristic algorithms are designed based on the feature of network structure to solve the formulated mixed integer nonlinear programming problem efficiently. Finally, simulation results demonstrate that by using the proposed strategy, the overall system cost is significantly reduced, decreasing by 78% and 82% compared to the cases without considerations of system dynamics and without cooperative transmission, respectively.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".