A discrete double-controller scheme for delayed processes with both load and set-point disturbances
Bibliographic record
Abstract
Using a double-controller strategy and a design approach related to Dahlin’s controller, a new sampled data control scheme is presented that is suitable for handling both set-point change and load disturbances. This control scheme has two controllers, a set-point controller and a load controller, which result in the separation of the load response from the set-point response in a closed-loop system. These two controllers can be designed independently to achieve good system performance for both set-point tracking and load rejection. The control scheme is applicable to processes that can be approximated by first-order plus time delay dynamics. For set-point changes, the set-point controller is a generalized Dahlin controller that has an extra tuning parameter T LC and has more ‘exibility and more robustness than Dahlin’s controller. For load disturbances, the load controller is also a generalized Dahlin controller and shows a significant improvement over the performance of Dahlin’s controller. The new double-controller scheme also alleviates a difficult compromise that the generalized Dahlin controller makes between the set-point tracking performance and load rejection performance. A simulation study is used to evaluate the performance of this new double-controller scheme in the presence of noise and model errors, and to compare it to Dahlin’s controller and the generalized Dahlin controller. The results show that the proposed double-controller scheme is superior to both Dahlin’s controller and the generalized Dahlin controller.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".