Guaranteed bounds on the performance cost of a fast real-time suboptimal constrained MPC controller
Bibliographic record
Abstract
A fundamental limitation on the implementation of constrained model predictive control (MPC) is the excessive computational time required to evaluate the constrained controls at each sample interval. Prior results show that premature termination of algorithms which solve the quadratic programming (QP) subproblem associated with the computation of the MPC controller at each sampling interval, can be used to decrease the necessary CPU time, with favorable results, but there is no guarantee on the performance of such ad hoc methods. In this paper, a new supervisory algorithm is introduced in order to guarantee bounds on the performance of any non-feasible algorithm, which on solving the QP subproblem is terminated before convergence has occurred. This supervisory algorithm allows for real-time control of a large class of systems using a suboptimal constrained MPC controller with guaranteed bounds on its performance. An example is included to illustrate how this method performs under the limitations of real-time control.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".