Development of high‐performance shake tables using the hierarchical control strategy and nonlinear control techniques
Bibliographic record
Abstract
Summary Conventional shake tables employ linear controllers such as proportional‐integral‐derivative or loop shaping to regulate the movement. However, it is difficult to tune a linear controller to achieve accurate and robust tracking of different reference signals under payloads. The challenges are mainly due to the nonlinearity in hydraulic actuator dynamics and specimen behavior. Moreover, tracking a high‐frequency reference signal using a linear controller tends to cause actuator saturation and instability. In this paper, a hierarchical control strategy is proposed to develop a high‐performance shake table. A unidirectional shake table is constructed at the University of British Columbia to implement and evaluate the proposed control framework, which consists of a high‐level controller and one or multiple low‐level controller(s). The high‐level controller utilizes the sliding mode control (SMC) technique to provide robustness to compensate for model nonlinearity and uncertainties experienced in experimental tests. The performance of the proposed controller is compared with a state‐of‐the‐art loop‐shaping displacement‐based controller. The experimental results show that the proposed hierarchical shake table control system with SMC can provide superior displacement, velocity and acceleration tracking performance and improved robustness against modeling uncertainty and nonlinearities. Copyright © 2015 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".