Distributed adaptive high‐gain extended Kalman filtering for nonlinear systems
Bibliographic record
Abstract
Summary In this work, we propose a distributed adaptive high‐gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high‐gain extended Kalman filter is designed for each subsystem. The distributed Kalman filters communicate with each other to exchange estimated subsystem state information. First, assuming continuous communication among the distributed filters within deterministic form of subsystems, an implementation strategy that specifies how the distributed filters should communicate is designed and the detailed design of the subsystem filter is described. Second, we consider the case of stochastic subsystems for which the designed subsystem filters communicate to exchange information at discrete‐time instants. A state predictor in each subsystem filter is used to provide predictions of states of other subsystems. The stability properties of the proposed distributed estimation schemes with both continuous and discrete communications are analyzed. Finally, the effectiveness and applicability of the proposed schemes are illustrated via the application to a chemical process example. Copyright © 2017 John Wiley & Sons, Ltd.
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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) | 0.000 | 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".