Experiment-based Performance Improvement of State Feedback Control Systems for Single Input Processes
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Bibliographic record
Abstract
This paper gives an extension to the Iterative Feedback Tuning (IFT) approach that ensures the performance improvement of state feedback control systems for single input processes. IFT employs sensitivity functions and the experiments conducted on the real-world control system in order to provide an efficient way to deal with the nonlinear or ill-defined processes when the model-dependent Linear-Quadratic Regulator (LQR) is not successful. An experimental setup is suggested to implement the real-time iterative calculation of the gradients in the minimization of the LQR's objective function. The experimental results validate the performance of the proposed IFT algorithm in a mechatronics application which deals with the angular position controller for a DC servo system with actuator dead zone and control signal saturation. The results show the reduction of the LQR's objective function for a single input process application.
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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