Enlarge your region of attraction using high-gain feedback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it