Data Synchronization for Offline and Online Identification of Dynamic Systems
Bibliographic record
Abstract
System dynamics identification has an important role in engineering, whether it is for used for modeling dynamic objects or mechanisms, controller design, or simulation of dynamic systems. The accuracy of estimation certainly depends on how the input variables used for estimation are obtained and synchronized in time. For systems such as actuators where usually only position is measured, the velocity and acceleration input variables are obtained in discrete-time domain through difference equations that shift the signals in time. In this way, the incorrect data synchronization in time might become an issue; likewise in online identification where filters used might cause significant phase delay. In this paper, the effect of discrete data synchronization for offline and online identification of dynamic systems is studied. The identification process is performed utilizing the widely-known batch Least Squares (LS) method and Recursive LS for off-line and on-line identification processes. Four possible combinations of backward difference and forward difference techniques are utilized for computing velocity and acceleration. Simulations of linear-time-invariant mass-damper-spring system for ten sets of system parameters are used to identify the best way to synchronize the data within the four possible differentiation options. The performance of the four options are evaluated in terms of parameter convergence and force prediction in the root mean square error sense. The results are discussed and guidelines are presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".