Adaptive neural network control of flexible-joint robotic manipulators with friction and disturbance
Bibliographic record
Abstract
An adaptive control strategy has been developed for flexible-joint robotic manipulators in the presence of friction nonlinearities and external disturbances. As the exact inverse model is unrealizable for such systems, only an approximation can be found. The control strategy consists of a rigid linear in parameter model based feedforward controller that approximates the flexible-joint inverse model and a neural network feedback controller that compensates for parametric and modeling uncertainties such as, friction, flexibility, and disturbance. A reference model is used as a trade off strategy to alleviate joint elasticity effects. Unlike other control strategies, no a priori offline training or weights initialization is required. Results with different situations highlight the performance of the adaptive controller in compensating for structured and unstructured dynamical uncertainties, in particular nonlinear Coulomb friction terms and external disturbance. Internal stability, a potential problem with such a system, is also verified. Furthermore, the adaptive control structure stability is guaranteed by Lyapunov stability theory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".