Passivity‐based control design frameworks for hybrid nonlinear time‐varying dynamical systems
Bibliographic record
Abstract
Abstract This article proposes two novel passivity‐based control design frameworks for hybrid nonlinear dynamical systems involving an interacting mixture of continuous‐time and discrete‐time dynamics whose dynamical properties evolve periodically over time. By deriving the Kalman–Yakubovich–Popov (KYP) conditions characterizing dissipativeness for hybrid nonlinear time‐dependent dynamical systems, a hybrid computational algorithm, which alternates between continuous‐time and discrete‐time subsystems at an appropriate sequence of time instants, is then proposed to solve the resultant equations in an interacting manner. Two passivity‐based control schemes are then developed by utilizing the foregoing KYP conditions in tandem with the passivity theorem. The overall framework consists mainly of three steps. The hybrid output dynamics of the plant are first determined judiciously to satisfy the passivity specifications. A hybrid nonlinear controller is then designed to meet the input strict passivity requirements. The stability of the closed‐loop system is finally established by interconnecting the plant and the controller through negative feedback. Practical considerations for appropriately implementing the derived hybrid algorithms are then discussed in detail. The efficacy of the proposed control schemes is ultimately assessed via a multi‐dimensional system with a hybrid source of actuation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".