Discrete-time parallel robot motion control using adaptive neuro-fuzzy inference system based on improved subtractive clustering
Bibliographic record
Abstract
This paper addresses a high precision discrete-time model-free PID adaptive neuro-fuzzy logic motion controller in case the physical models that describe a robot are not known. The advantage of this kind controller is that it uses an improved subtractive clustering technique to obtain the structure of the system model in order to ensure the high accuracy of the intelligent control. Moreover, the information used is directly from the nonlinear system response without the knowledge of the robot physical parameters and complex models. Furthermore, the controller is designed in discrete-time domain for allowing its implementation. To conceive this kind intelligent control, first adaptive neuro-fuzzy inference system with an improved subtractive clustering computing is used to accomplish the integration of information of joint angular displacement and velocity for torque identification. The learning datasets are generated by using a discrete-time PID feedback control, which desired position is sufficiently rich to ensure that the learning system would include most characteristics of the nonlinear dynamics of the system. Then a discrete-time fuzzy feed forward control, combined with a conventional PID and the adaptive neuro-fuzzy inference system, is designed for the mechanism motion control. Simulation results from numerical experiment on a 4-bar planar parallel mechanism show the proposed controller can reduce joint position and velocity tracking errors with higher accuracy and higher reliability than a traditional PID controller and a computed torque controller.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".