Position control of a flexible joint with friction using neural network feedforward inverse models
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Bibliographic record
Abstract
This paper presents a proposition of a control strategy based on artificial neural networks for mechanisms with hard nonlinearities. The parallelism, the regularity and the ability to approximate nonlinear functions of neural networks make them good candidates for this control task and for real-time and VLSI implementation. The flexible joint model includes Coulomb and static frictions for both motor and load and the model is used in learning and generalization phases of the neural network inverse model of the mechanism. The control structure includes an inverse model based feedforward neural network controller and a partial state feedback control law that consists of a fuzzy sliding mode control law. Simulation results show the performance of the controller, its robustness with respect to load inertia variations and its fast response to mismatch in load position initial condition.
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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