Overcoming passivity violations: closed‐loop stability, controller design and controller scheduling
Bibliographic record
Abstract
Control of systems that have had their passive input–output map partially violated is the motivation of this study. The hybrid passivity/finite‐gain systems framework is specifically well suited to systems that have experienced a passivity violation. The focus of this study is the extension and application of the hybrid passivity/finite‐gain systems framework to a multi‐input multi‐output (MIMO) control problem. Calculation of the hybrid passivity/finite‐gain parameters in a linear time‐invariant (LTI) MIMO context is considered. Additionally, we show that a set of hybrid very strictly passive/finite‐gain (VSP/finite‐gain) controllers gain‐scheduled in a particular way also possesses hybrid VSP/finite‐gain properties. To synthesise hybrid VSP/finite‐gain controllers a frequency‐weighted optimal control scheme is used to parameterise controllers that are then constrained and optimised within a numerical optimisation framework. The theoretical contributions of this work are validated experimentally using a two‐link flexible manipulator apparatus. Results highlight the utility of the hybrid passivity/finite‐gain framework, the scheduling scheme and controller design method.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".