Acceleration‐based sliding mode hierarchical control algorithm for shake table tests
Bibliographic record
Abstract
Abstract Existing control algorithms for seismic shake table tests (STTs) generally exhibit limitations such as poor acceleration tracking for displacement control, instability that results in table drift for direct acceleration, force, or velocity control, and the lack of a theoretical justification for hybrid control. Therefore, a reliable control algorithm has become key for effective shake table control. This paper presents acceleration‐based sliding mode control (SMC) as a solution to the drawbacks of the traditional force‐based SMC; in this manner, the influence of the force of the tested structure applied on the table as well as unmodeled complex nonlinear forces, such as friction, are counteracted. An acceleration‐based sliding mode hierarchical control (ASMHC) algorithm is proposed, where the acceleration‐based SMC is used as the high‐level controller to generate the corrected acceleration command, and the low‐level controller, that includes feed‐forward and feedback control, tracks the acceleration command in real time. The high‐level controller, having zero asymptotic stability, and the low‐level controller, designed based on the system transfer function, ensure tracking stability in time and frequency domains, respectively. The proposed ASMHC algorithm was first verified by a series of bare STTs, and was then applied to a real STT of a two‐story steel structure. The experimental results show that the proposed ASMHC algorithm can achieve good tracking of displacement, velocity, and acceleration in both time and frequency domains, which ensures accurate reproduction of seismic excitation in STTs.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".