A Novel Approach to Build a Learning Controller by Combining Fuzzy Modeling, Sliding Mode, and PID Control for Robotic Applications
Bibliographic record
Abstract
This paper presents a novel approach to combine three powerful methods of control theory and fuzzy c-means clustering algorithm to build a robust learning control for nonlinear uncertain systems such as advance robot manipulators. It is worth noting that the combination not only encompasses the features and capabilities of its components but also the limitations attributed to these techniques (e.g, stability issues of fuzzy controller and poor performance of PID controller in the presence of time-varying uncertainties) may be remedied by each other. To date different combinations of the above mentioned methods presented in the literature each having its own merits and limitations. But, for advance robotics applications such as medical robot, and space applications and with increasing complexity of the robot’s tasks, there is a pressing need for the control systems that are able to learn systematically and efficiently during the course of operation, from its own experience, from the demonstration and also in an unsupervised fashion. The controller proposed in this paper aims at building such a control system using a novel approach to combine adaptive fuzzy modeling algorithm, fuzzy c-means clustering algorithm, adaptive sliding mode control and PID controller. Based on the simulations and experimental results, the proposed controller performs remarkably well in terms of the tracking error convergence and robustness against uncertainties.
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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".