Application of Adaptive Discrete Feedforward Controller in Multi-Axial Real-Time Hybrid Simulation
Bibliographic record
Abstract
Real-time hybrid simulation (RTHS) evaluates the dynamic performance of a structure by physically testing the selected components while modeling the remaining structure numerically, making it efficient in both cost and testing space requirements. In RTHS, accurately imposing target boundary conditions on specimens is critical, as it directly influences test accuracy and overall simulation stability. However, boundary condition application often experiences tracking errors due to the dynamics of the servo–hydraulic loading system and control-structural interaction. This challenge intensifies with multiple actuators operating in a multi-axial setup, introducing dynamic coupling effects. Thus, an outer-loop controller enabling precise actuator tracking of reference boundary conditions is essential for reliable RTHS. While advancements in outer-loop controllers for uniaxial RTHS exist, multi-axial RTHS (maRTHS) employing multiple degrees of freedom control remains underexplored. This study applies the adaptive discrete feedforward controller (ADFC), consisting of a discrete feedforward compensator and an online identifier, to a multi-input, multi-output (MIMO) system for maRTHS. To validate ADFC’s performance and robustness, 1000 virtual maRTHS tests incorporating plant uncertainties were conducted under seismic excitations. Ten evaluation criteria were applied. Results confirm that ADFC achieves robust and stable control by reducing phase and amplitude errors, while also improving estimation accuracy at the physical–numerical interface.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".