Model reference adaptive hierarchical control framework for shake table tests
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".