Energy‐based output regulation for stochastic port‐Hamiltonian systems
Bibliographic record
Abstract
Abstract This article investigates the output regulation for stochastic port‐Hamiltonian systems (SPHSs) subject to sinusoidal disturbances. An energy‐based regulation scheme with an internal model unit is proposed by exploiting the stochastic Hamiltonian structure, which drives the tracking error to the origin while maintaining asymptotical stability in probability of the closed‐loop system. An energy‐based robust regulation scheme as well as an alternative condition is then developed without solving Hamilton–Jacobi–Issacs inequalities. The proposed regulators preserve the stochastic Hamiltonian structure of the disturbed SPHS by coordinate transformation. Hence the output regulation problems fall into the stabilization framework for SPHSs and there is no need to solve regulator equations. These results cover the stabilization of SPHSs and the output regulation of deterministic port‐Hamiltonian systems. Simulations on an inverted pendulum show the effectiveness of the proposed methods.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".