Model reference adaptive hierarchical control framework for shake table tests
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Bibliographic record
Abstract
Abstract The structural response under earthquake excitation can be simulated by shake table tests. However, the performance of the shake table is affected by the Control‐Structure Interaction (CSI) effect. In recent years, nonlinear control algorithms were developed to compensate for the CSI effect. In this study, a model reference adaptive control algorithm, named model reference adaptive hierarchical control (MRAHC) framework, is presented. MRAHC consists of a high (adaptive) and low (loop‐shaping) level controller. The high‐level (adaptive) controller develops the control algorithm on the system level, which directedly considers the inherent nonlinearity of the test specimen and the CSI effect. While the low‐level (loop‐shaping) controller develops the control algorithm to regulate the hydraulic system and make sure it can follow the reference signal generated by the high‐level (adaptive) controller. MRAHC offers many advantages including direct compensation to the structural nonlinearity and the ability to handle the CSI effect. In addition, it allows users to quantify the mass of the test specimens without measurement. To evaluate the performance of the MRAHC method, shake table tests with different upper structure masses were carried out. The performance of the MRAHC was compared with the direct loop‐shaping control method (LC) and the Proportional‐Integral‐Differentiation control method (PID). The results show that the MRAHC can achieve better acceleration tracking compared to the LC and PID control methods. Hence, the MRAHC can be used as an effective nonlinear controller for shake table tests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it