Constrained MPC under closed-loop uncertainty
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
The importance of closed-loop uncertainty predictions in robust model-predictive control (MPC) has been discussed by a number of authors in previous years [(A. Bemporad, 1998), (D. Mayne, 2000), (B. Kouvaritakis, 2000)]. The proposed controllers often rely upon invariant sets and require that input constraints are inactive at the final steady-state [(B. Kouvaritakis, 2000), (M. V. Kothare et al., 1996)]. The controller discussed in this paper avoids this limiting assumption while maintaining robust output constraint handling. This paper emphasises the often negative effects of probabilistic input constraints and proposes a method based upon multiple uncertainty regions to deal with these effects. The proposed controller solves a second-order cone program (SOCP) at each execution in order to determine the set of control moves that optimizes the expected performance of the closed-loop system while maintaining the uncertain process outputs and inputs within their allowable bounds. Case studies illustrate the performance of the new controller when plant/model mismatch is present.
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