Artificial neural network based model predictive control: Implementing achievable set‐points
Bibliographic record
Abstract
Abstract This paper addresses the problem of determining achievable set‐points for artificial neural network (ANN)‐based model predictive control (MPC) designs. In particular, this work considers a case where a first‐principles model may not be readily available for a nonlinear process, while sufficient closed‐loop data containing possibly correlated outputs is available, such that an ANN‐based model that captures the nonlinear dynamics reasonably well can be identified. The paper addresses implementation aspects with such an ANN‐based MPC design—specifically that of ensuring that achievable set‐points are prescribed to the MPC. The key idea is to perform principal component analysis (PCA) on the training data in order to recognize existing collinearity and determine the upper confidence limit of squared prediction error (SPE) statistic. An optimization problem subject to the SPE constraint is then defined to calculate the achievable set‐points, that can in turn be provided to an MPC design. The efficacy of the proposed approach is illustrated via implementations on a chemical reactor example. The results reveal the superior tracking performance of MPC using the achievable set‐points over the case where arbitrarily prescribed set‐points are used in the MPC implementations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".