Comparison of SVSF-KF Adaptive Estimation Algorithms on an Electrohydrostatic Actuator Subject to a Fault
Bibliographic record
Abstract
State estimation strategies are vital for obtaining knowledge of a dynamic system’s state when faced with limited measurement capability, sensor noise, or uncertain system dynamics. The Kalman filter (KF) is one of the most widely recognized filters and provides the optimal solution for linear state estimation problems. The smooth variable structure filter (SVSF) is a model-based strategy that is also formulated as a predictor-corrector. Despite being a suboptimal estimator, the SVSF is highly robust to modeling uncertainties, errors, and system change. The combination of the SVSF with the KF (SVSF-KF) results in an adaptive estimation algorithm that provides an optimal KF estimate in normal operating conditions, and a robust SVSF estimate in the presence of faults or uncertainties. While effective in some cases, the SVSF-KF has been shown to suffer from several drawbacks associated with the time-varying smoothing boundary layer (SBL) and adaptive gain used to detect system change. Several new approaches have been proposed in recent years with the aim of improving the SVSF-KF’s performance. Among these approaches is a novel gain formulation based on the normalized innovation squares (NISs), while another makes use of the interacting multiple model (IMM) framework. In this article, we review the newly proposed SVSF-KF formulations and compare their performance on an electrohydrostatic actuator (EHA) test case.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".