A disturbance reduction scheme for linear systems with time delays and modeling uncertainties
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Bibliographic record
Abstract
A disturbance reduction scheme for linear systems with time delays and modeling uncertainties is presented in this paper. Unlike other disturbance rejection methods, the proposed scheme does not require information about unknown disturbance frequencies. The linear systems in this study are modeled to be nominally stable, minimum phase and relative degree one systems. The control structure is based on Astrom's modified Smith predictor with the proposed scheme consisted of an input disturbance reduction controller (IDRC) and a residual disturbance reduction controller (RDRC). The IDRC using an artificial neural network (ANN) is proposed to reduce an unknown input disturbance including unknown load disturbances and modeling uncertainties in both stable and unstable systems. The ANN can approximate appropriately a product of an inverse of a time delay and a nonnegative gain in the IDRC. In addition, the undesired responses caused by residual disturbances and residual modeling uncertainties are suppressed by the RDRC. Simulation results show the effectiveness of the presented disturbance reduction scheme for linear delay systems with modeling uncertainties, subjected to periodic unknown load disturbances.
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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 it