Decentralized pole-placement using generalized sampled-data hold functions
Bibliographic record
Abstract
This paper presents a novel sequential technique for pole-assignment in linear time-invariant (LTI) decentralized control systems. Generalized sampled-data hold functions (GSHF) are used as local controllers to place the modes of the equivalent discrete-time closed-loop system in the desired locations in the z-plane. These locations are assumed to be obtained by using a proper mapping from the continuous-time domain. The GSHFs are obtained one at a time, in a sequential fashion. In other words, each local controller is designed for the equivalent discrete-time closed-loop model associated with the previously designed controllers. While no bound is provided on the intersample ripple, the convergence of the samples to zero ensures that the intersample values will also approach zero as time increases. The main characteristic of the proposed method is that unlike conventional pole-placement algorithms, the design complexity here does not increase after each local controller is obtained. A numerical example is provided which confirms the efficacy of the proposed pole-placement technique.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".