A neural networks controller for a single-link flexible manipulator based on the inverse dynamics structure
Bibliographic record
Abstract
Motivated by the well-known inverse dynamics control structure developed in the literature for flexible link manipulators, in this paper two multi-layer neural networks (NNs) are proposed to learn the nonlinearities of the system for achieving tip position trajectory tracking control for a single-link flexible manipulator. The re-defined output approach is used by feeding back this output to guarantee the minimum phase behavior of the resulting closed loop system. No a priori knowledge about the nonlinearities of the system is needed where the payload mass is also assumed to be unknown. The weights of the networks are adjusted using a modified online error backpropagation algorithm that is based on the propagation of output error, derivative of error and the tip deflection of the manipulator. The real-time controller is implemented on an experimental setup. The results achieved by the proposed neural network (NN) controller are compared experimentally with conventional PD and inverse dynamics controls to substantiate the advantages of our scheme and its promising potential.
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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".