A state space-based explicit integration method for real-time hybrid simulation
Bibliographic record
Abstract
Real-time hybrid simulation (RTHS) combines experimental testing with numerical simulation. It provides an alternative method to evaluate the performance of structures subjected to dynamic loading such as earthquake or wind. During RTHS, a numerical integration algorithm is employed to directly solve the equation of motion and generate command displacements for the experimental test structure online. This paper presents an improved explicit numerical integration method for RTHS based on discrete state space formulation. The improved integration method utilizes an extrapolation-based prediction procedure for command displacement by applying a zero-order hold. The predicted command displacement is then used to compute the final command displacement by applying a first-order hold. Both the stability and the accuracy of the proposed integration method are investigated using control theory and numerical and experimental simulations. The proposed method demonstrates improved performance compared with other integration methods. The robustness and feasibility of the proposed method were verified through experimental RTHS on two different computational platforms including a National Instruments system and a dSPACE embedded controller. The proposed method may also be implemented on other platforms containing an xPC target and MATLAB environment. By placing the algorithm on the dSPACE system, any dynamically rated actuator with a controller that can receive analog signal may be used for RTHS. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.005 | 0.001 |
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".