Disturbance Rejection Using a Simplified Predictive Control Algorithm
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Bibliographic record
Abstract
A disturbance predictor was proposed earlier for a simplified model predictive control (SMPC) algorithm (Gupta, Y. P. Comput. Ind . 1993, 21, 255). In this paper, a closed-loop transfer function is derived for the proposed predictor, and its performance is analyzed. The analysis shows that the predictor offers an improvement in disturbance rejection. A new optimization scheme is proposed for online determination of a tuning parameter employed in the disturbance predictor. The disturbance predictor is applied to three example problems for nonstationary ARIMA disturbances that commonly occur in many industries. A comparison with the generalized analytical predictor shows that the proposed disturbance predictor provides an improved control performance. In addition, a relationship between the tuning parameter of the Dahlin and SMPC algorithms is presented. This relationship enables the selection of the tuning parameter of the SMPC algorithm by using a desired value of the closed-loop time constant.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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