Nonlinear filtering based on lattice trajectory piecewise linear approximation with application to a wastewater treatment plant
Bibliographic record
Abstract
State estimation is a vital part of state-feedback controller design. The extended Kalman filter (EKF) approximates the original nonlinear system through successive linearization. The linearization points are selected at each sample instant, which is computational complex and may not reflect the overall trend of the nonlinear system. In this paper, we propose a nonlinear filtering method based on lattice trajectory piecewise linear (LTPWL) approximation, named LTPWL-KF, in which the nonlinear system is approximated by the LTPWL model offline, and the online state estimation is then based on the constructed piecewise linear (PWL) system. The boundedness of the variances of the estimation error is proved. A simulation study on a wastewater treatment plant (WWTP) is performed. The results show that the estimation performance of LTPWL-KF is comparable with that of EKF, and the online computational burden of LTPWL-KF is less.
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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".