Coordinated distributed moving horizon state estimation for linear systems based on prediction‐driven method
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 particular, the class of linear system we focus on is composed of several subsystems that interact with each other via their states. 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, and 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 presented to illustrate the applicability and effectiveness of the proposed scheme. The cases with communication failures, and premature termination are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".