Signal processing in iterative improvement of inverted pendulum crane mode control system performance
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Bibliographic record
Abstract
This paper discusses signal processing aspects concerning the iterative experiment-based improvement of the performance of crane mode control systems of inverted pendulum systems. The control system performance specifications are imposed in terms of a reference model, and the objective is to force the output error defined as the difference between the cart position as controlled output and the model reference output to tend to zero. An optimization problem with an objective function defined as the sum of squared output errors is defined. Iterative Feedback Tuning (IFT) algorithms are employed to solve the optimization problem where the variables are the controller tuning parameters. Four-parameter discrete-time linear controllers are proposed and tuned by IFT algorithms. The real-time experimental results highlight the performance improvement ensured by the position control system with the new IFT-based linear controller with respect to the initial proportional-integral one.
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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 | 0.000 |
| 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 | 0.000 | 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