Multi‐rate real‐time hybrid simulation with adaptive discrete feedforward controller‐based compensation strategy
Bibliographic record
Abstract
Abstract Real‐time hybrid simulation (RTHS) is an innovative experimental testing technique that tests only some structural components for which accurate numerical models do not exist (i.e., experimental substructure), while modeling the rest of the structure numerically (i.e., numerical substructure). Conventionally, the two substructures in RTHS are executed at the same rate and interact in real time. Multi‐rate real‐time hybrid simulation (mrRTHS) is a modification of RTHS where different time step sizes are applied to the numerical substructure computation and the actuator control. This configuration provides a larger allowance for real‐time execution, thereby enabling the use of higher‐fidelity numerical models in RTHS. However, it complicates the data transmission between the substructures and introduces an additional time delay. The increased delay consists of communication and computational delays, which are independent of actuator dynamics. This study compares the data transmission within RTHS and mrRTHS configurations and confirms that mrRTHS experiences a larger delay error. To improve the accuracy of mrRTHS, a time‐varying interpolation built with Chebyshev polynomials is used. Additionally, a compensation strategy based on an adaptive discrete feedforward controller is introduced to compensate for actuator‐induced tracking errors and the increased time delay in mrRTHS. A demonstrative experimental study is conducted to investigate the seismic behavior of a large‐scale base‐isolated structure equipped with gyromass dampers. The results indicate that the proposed mrRTHS compensation strategy facilitates a more realistic assessment of the dynamic performance of structural systems by employing higher‐fidelity numerical substructures in mrRTHS.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".