Tracking and Disturbance Rejection of Extended Constant Signals with Unknown Disturbance Structure Using MPC
Bibliographic record
Abstract
This paper considers the Model Predictive Control (MPC) set point tracking/regulation problem for a discrete LTI system, which is subject to a class of unbounded disturbances/tracking signals called extended constant signals of unknown structure. Examples of disturbances which belong to this class include constant disturbances as well as unbounded signals such as w[k]=√k and log (k), k=1,2,3,…. A discussion re the choice of window size for MPC is also made; in particular, it is shown that the window size must be larger than a certain lower bound, which can be easily determined, in order to guarantee closed loop stability in MPC control. The main contribution is a formulation of the system's plant equations under which, for output regulation, no knowledge of the structure or magnitude of disturbances is needed in order to achieve set point regulation for this class of extended constant signals. The result is of interest since it also implies that no disturbance observer is necessary in order to solve the set point tracking/regulation problem when full-state feedback is available. The results are experimentally verified.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".