Bibliographic record
Abstract
This thesis focuses on the modelling and optimal control of a load-controlled fatigue structural testing rig. The modelling phase involves first attempting to analytically model the test system and the test controller, then using these models to estimate test cycle times. Afterwards, a system identification approach is taken to generate a more reliable numerical model using data. Open-loop and closed-loop methods are discussed, although only closed-loop experiments can be performed on fatigue testing rigs to prevent unnecessary damage to the valuable test article. The direct, indirect, and dual-Youla closed-loop system identification methods are applied to measurement data from a fatigue testing rig at the National Research Council of Canada (NRC). The identified models are validated then used in various controller synthesis methods. First, two methods for generating “optimal” proportional-integral (PI) gains are presented. The first uses H∞-optimal static output feedback, and the second employs the Bounded Real Lemma, iteration, and bisection method. Next, a single-input-single-output (SISO) approach to designing two degree-of-freedom (2DOF) controllers is presented. The feedback controller can be a PI or H∞ controller, for example, and the feedforward controller is designed using an approximate inverse of the plant transfer function. Finally, a multi-input-multi-output (MIMO) 2DOF H∞-optimal controller synthesis method is described. The alternative controllers are implemented on the test rig and used to perform tests. Tracking results and their comparison to the standard PI controller are presented
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".