A mixed-integer programming approach to networked control systems
Bibliographic record
Abstract
This paper studies the problem of controller design for networked control systems regulated by a network data transmission protocol proposed in [50]. In this framework, the plant is first formulated as a mixed logical dynamical (MLD) system, then model predictive control (MPC) based on the mixed-integer programming is adopted to design a controller to guarantee certain control performance. It is shown that the solvability of the finite-horizon MPC is not equivalent to that of the infinite-horizon MPC, which is normally true for most existing MPC methods. The non-convexity feature of this type of networked control systems rules out explicit piecewise affine controllers that are designable for linear convex control systems. Notwithstanding these diffculties, controller design is still feasible due to the special nature of the data transmission strategy, i.e., only a small number of logic values are involved. Furthermore, control of higher-order systems and tracking of more complicated signals can be readily dealt with using this new approach. Two examples are presented to illustrate the strength of the proposed approach.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".