LEARNING DIGITAL CONTROL DESIGN MADE EASY THROUGH REAL-TIME EXPERIMENT SOLUTIONS
Why this work is in the frame
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Bibliographic record
Abstract
When teaching digital control course, it s found that students are often struggling with understanding the connection between the underlying mathematics for various control algorithms and their implementation. In particular, the effect of the control algorithm on system seems to be a mystery. Matlab simulation is able to help students better understanding the control system and building up confidence in the effectiveness of the controller. However, simulation alone is not able to get rid of questions such as “is it really going to work on real system?” or “how is it going to work in real-life?”. This paper describes the integration of real-time experiment solutions into the digital control course offered in the School of Engineering at University of British Columbia Okanagan and gives detailed presentations on real-time implementation of digital control algorithms. In particular, implementation of LQG will be demonstrated. The impact on teaching and learning will also be discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it