Bibliographic record
Abstract
"Future industrial systems will require control systems to be more reliable, autonomous, robust, and yet efficient. The emphasis of this thesis is to introduce a robust control algorithm definition that addresses the needs of future industrial environments. The proposed controller is based on an adaptive concept with a two-step approach. In step one, the system model is identified in a closed-loop by a robust technique. In step two, the obtained system model from step one is used to formulate a robust controller. The system identification is the central part of the controller design since the controller can only be as good as the model that is used to design it. In order to improve the performance and robustness of the system identification, this thesis proposes expert system supervised multiple system identifications. The role of the expert system is to periodically evaluate the estimated models and to propose-one for the controller design. The robust controller is formulated by the H00 (sub )optimal design procedure using the proposed system model. The idea behind this controller design technique is to combine an on-line identification algorithm with a control design method that yields a time-varying controller which follows the changing plant. The effectiveness of the proposed robust controller in an industrial environment is demonstrated by simulation and experimental tests. The proposed robust controller as a power system stabilizer has been tested by simulations on a power system model and in the experimental environment using the micro-synchronous generator at the University of Calgary. "
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".