Bibliographic record
Abstract
Modelling physical systems is one of the main technical challenges in the field of control systems and haptic devices. In this work, a two part methodology is proposed that generates a model which employs qualitative reasoning to encapsulate nonlinear effects that are often approximated as linear processes. We utilize fuzzy set theory to implement a rule-base that has been constructed from both conventional and expert knowledge to model the nonlinear damping behaviour of the PHANToM/spl trade/ haptic device. Our methodology is used to produce an estimate of the nonlinear parameters in a mathematical model a PHANToM/spl trade/ haptic device integrated with a rate feedback controller. Most estimation methods approximate damping factors using a linear approximation based on experimental data. In the first part of our method, a rule-based expert system is developed based on constant parameters which have been estimated from experimental data (linear model) and the system parameters are tuned for multiple operating regions. The second part of our method develops an expert system using constant parameters based on expert knowledge. Several system responses are examined to show the ability of our technique to capture a variety of conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".