Coordinated distributed MHE for linear systems
Bibliographic record
Abstract
The distributed framework has been considered as one promising framework for the control of large-scale systems. In this work, we propose a coordination algorithm for distributed moving horizon state estimators (MHEs) for discrete-time linear systems composed of subsystems. In the proposed coordinated distributed MHE (CDMHE) scheme, each subsystem is associated with a local MHE. In the design of a local MHE, a coordinating term is incorporated into its cost function which is determined by an upper-layer coordinator. At each sampling time, a local MHE estimates its local state and system noise, then sends them to the coordinator. The coordinator calculates a price vector based on information received from all the local MHEs and sends the price vector together with the calculated interaction estimates to each local MHE. The above steps are performed iteratively every sampling time. It is shown that the CDMHE scheme is able to achieve the estimation performance of the corresponding centralized design if convergence at each sampling time is ensured. A simulation study based on a chemical process is used to illustrate the applicability and effectiveness of the proposed scheme.
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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".