ANN-based adaptive motion and posture control of an inverted pendulum with unknown dynamics
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an artificial neural network (ANN) based control scheme is introduced for the inverted pendulum motion and posture control problem. The adaptive control strategy consists of a Lyapunov stability-based online weights adaptation that provides asymptotic tracking while learning the nonlinear inverted pendulum system's dynamics. Unlike other control strategies, no a priori offline training, weights initialization, or parameters knowledge is required. Experiments for different situations highlight the performance of the proposed controller in compensating for friction nonlinearities, in the form of Coulomb friction. Furthermore, the neural networks inherent parallelism makes them a good candidate for implementation in real-time electromechanical systems.
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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