Data-driven Model-Free Adaptive Control Tuned by Virtual Reference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a new tuning approach, by which, all parameters of a datadriven Model-Free Adaptive Control (MFAC) algorithm are automatically determined using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. The approach is referred to as mixed MFAC-VFRT control and it leads to mixed MFAC-VFRT algorithms. An advantage of mixed MFAC-VFRT control, is that it combines systematically, the features of VRFT (it computes the controller parameters using only the input/output data) with those of MFAC. This is especially illustrated by comparison with the classical MFAC algorithms, the initial values of the parameters, which are obtained through a process that involves solving an optimization problem. The application that validates the mixed MFAC-VFRT algorithms, by experiment, is a nonlinear twin rotor aerodynamic system laboratory equipment position control system, that represents a tribute, to Prof. Antal (Tony) K. Bejczy for his excellent results in space robotics, robot dynamics and control, haptics and force perception/control.
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.
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 | 0.001 |
| Open science | 0.002 | 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