MV bound and MV controller for convex‐non‐linear systems with input constraints
Bibliographic record
Abstract
To assess the performance of a control loop based on the minimum variance (MV) benchmark, we need to calculate MV lower bound (MVLB). Even though there is a plethora of literature available for calculating MVLB for the linear systems, these methods are not suitable for non‐linear systems. Furthermore, almost all of the real‐world applications have been encountered with input variance constraints. These constraints limit controllers' abilities in decreasing the output variability. Therefore, existing MVLB computation methods, which do not account for input constraints, are not realistic when applied to constrained systems. The authors propose a novel approach to estimate MVLB by employing properties of dual Lagrangian functions to address these issues simultaneously in this study. Furthermore, to design the constrained non‐linear MV controller (MVC), they propose to use the recurrent neural network for accommodating non‐linearities and the input constraints. Then, control loop stability, optimality with respect to MVLB as well as the global convergence of the proposed controller are analytically proved for convex‐non‐linear systems with input constraints. The proposed control strategy is verified through simulations performed on a non‐linear quadruple‐tank system. The results indicate that the proposed design provides satisfactory results in decreasing output variance while satisfying the constraints.
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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 | 0.004 |
| 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) | 0.002 | 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".