Artificial Neural Network Control of a Flexible-Joint Manipulator Under Unstructured Dynamic Uncertainties
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Bibliographic record
Abstract
This paper proposes a position control strategy based on artificial neural networks (ANN) in the face of structured and unstructured dynamic uncertainties. The control structure consists of a feedforward multilayer perceptron (MLP) to approximate the manipulator's inverse dynamics online, a feedback radial basis function (RBF) neural network to compensate for the residual errors, and a reference model that defines the desired error dynamics. The online adaptation of the RBF neural network is is accomplished through two methods: (i) the least mean squares (LMS), and (ii) the recursive least squares (RLS) algorithms. A comparison study is conducted to evaluate the efficiency of both algorithms on the tracking ability of the proposed control scheme. Simulation results highlight the performance of the proposed control structures in compensating for the highly nonlinear unknown dynamics of the manipulator and its robustness in the presence of model imperfections.
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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