Improved Gain Conditioning for Linear Model Predictive Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One challenge when using linear model predictive control (MPC) is that model mismatch and ill-conditioned gain matrices can lead to undesirable aggressive controller behavior. To address this issue, we propose improvements to an existing offline method for gain-matrix conditioning. The proposed algorithm identifies problematic manipulated variables (MVs) with correlated effects on controlled variables (CVs) and solves a constrained linear least-squares optimization problem to adjust the problematic gains. Additionally, the proposed algorithm prevents the optimizer from switching the signs of some gains and allows control practitioners to specify trusted key gains that should be held constant. We also extend the method to condition gain submatrices in scenarios where some of the CVs may temporarily be eliminated from the control problem. To illustrate the effectiveness of the proposed algorithm, we present a case study involving industrial fluidized catalytic cracking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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