ROBUST ADAPTIVE NEURAL FUZZY CONTROLLER WITH MODEL UNCERTAINTY ESTIMATOR FOR MANIPULATORS
Bibliographic record
Abstract
In this paper, a new robust adaptive neural fuzzy controller (RANFC) for manipulators is proposed. The proposed controller uses the fuzzy logic inverse dynamic model output (approximated torques/forces) as a model-based control term; a decentralized PID controller as a feedback term to enhance closed-loop stability and improve transient performance; and multi-layer feedforward neural networks (NNs) as a slow learning tool to generate new rules or modify the membership function of existing rules to compensate for the unmodelled dynamics such as flexibility and friction. Moreover, in order to guarantee the robustness and stability of the controller, a new adaptation scheme is introduced to compensate for the measurement noise, external disturbances with random pattern, and effects of rapidly time-varying parameters. Another key feature of this scheme is that a priori knowledge of the bounds of uncertainties is not required. The global stability and robustness of the proposed controller are established using Lyapunov’s approach and fundamentals of sliding mode theory. The simulation results illustrate the strength of the proposed controller in the presence of the model uncertainties and external disturbances.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".