Model-Predictive Control With Generalized Zone Tracking
Bibliographic record
Abstract
In this paper, we propose a new framework for model-predictive control (MPC) with generalized zone tracking. The proposed zone MPC tracks a generalized target set of system state and input which is not necessarily control-invariant. In this context, the classical MPC theory no longer applies because the target zone may not be stable in the sense of Lyapunov. We extend LaSalle's invariance principle and develop new theories for stability analysis of zone MPC. It is proved that under the zone MPC design, the system converges to the maximal control invariant set in the target zone. Sufficient conditions for asymptotic stability of the maximal control-invariant set are also discussed. By tracking the generalized target zone, the proposed zone MPC is able to: (i) yield smaller zone tracking errors than all existing methods which essentially track some steady-state subset of the target zone, and (ii) allow more admissible operations and release more degrees of freedom to achieve other economic objectives. Further discussions are made on extending the prediction horizon of the zone MPC based on an auxiliary control law as well as handling a secondary economic objective via a second-step economic optimization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".