A co-operative control approach to the regulation of nonlinear discrete-time structured systems
Bibliographic record
Abstract
This work extends previous research dealing with the deadbeat out put regulation of discrete-time nonlinear plants. The plant is composed of agents that interact, via scalar-valued signals, in a known structured way. As in our earlier research, control agents are introduced to interact with certain plant agents, where each control agent focuses on regulating a specific plant agent, called its target. Targeting analysis is used to determine if control laws can be found to regulate all target agents, then growing analysis is used to determine the effect of those control laws on non-target plant agents. Regulation is achieved if these analyses indicate that all plant agents can be regulated. This paper goes beyond previous work by extending the class of allowable plant dynamics. Moreover, new necessary and sufficient algebraic conditions are derived to determine when targeting succeeds, and new easily-verifiable conditions necessary for targeting and/or growing to succeed are presented. These new conditions concern the positioning of control agents and targets as well as the propagation time of signals through the plant.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".