Enlarge your region of attraction using high-gain feedback
Bibliographic record
Abstract
We can associate with the pseudo-linearization method of regulation a region of attraction, /spl Uscr//sub 0/, containing the equilibrium manifold of a nonlinear system. This paper discusses the use of high-gain feedback to force system trajectories into /spl Uscr//sub 0/. The control strategy is to switch from the high-gain controller to the pseudolinear controller once the state enters an estimate of /spl Uscr//sub 0/. This controller structure can increase the size of the region of attraction when compared to pseudolinearization alone. Sufficient conditions for the existence of the controller are presented, as is an algorithm for controller construction. The peaking phenomenon, which can arise because of the high gain, is investigated. Finally, the acrobot is presented as an application of the high-gain switching control. Simulations indicate the region of attraction is significantly enlarged, while computational complexity of the overall control law, both in terms of off-line construction and real-time implementation, is reasonable.>
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".