Multi-layer switching structure with periodic feedback control
Bibliographic record
Abstract
In this paper, a novel switching control architecture for linear time-invariant (LTI) systems using generalized sampled-data hold functions (GSHF) is proposed to reduce the magnitude of the transient response. It is assumed that the plant model belongs to a finite set of known models. The output of the system is periodically sampled and a control signal is being generated by using a suitable hold function from a set of GSHFs. A control architecture consisting of a layer of high-performance GSHFs (one for each plant model) and some other layers of simultaneous stabilizing GSHFs is introduced. It is shown that using the above sets of GSHFs and a proper switching path, one can reduce the number of switchings to destabilizing GSHFs. As a result, the transient performance which is the main shortcoming of most switching control schemes is improved using the proposed strategy. Furthermore, it is shown that using GSHFs instead of continuous-time controllers reduces the complexity of online computations required to obtain the upperbound signals. Simulation results show the effectiveness of the proposed method in improving the transient performance
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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) | 0.000 | 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".