Comparison Between an Intelligent Controller and a Sliding Mode Controller to Positioning Pneumatic Actuators
Bibliographic record
Abstract
Sliding Mode Control (SMC) technique is a well-established method in positioning pneumatic actuators due to its consistent performance in the presence of model uncertainties. Brain Emotional Learning Based Intelligent Controller (BELBIC) is a new model free controller with flexible structure and low computational load. It has been successfully applied to many control problems. In this work we study, for the first time, how well a BELBIC performs in comparison with SMC approach in positioning a pneumatic actuator. Different position tracking tasks are evaluated on a low-cost pneumatic actuator and in presence of significant friction. Comparison is done based on positioning accuracy, non-oscillatory motion and robustness to external load. The results show that while both controllers successfully track different trajectories, SMC is generally more accurate. BELBIC maintains its performance in the presence of large static friction. Furthermore, it produces less oscillatory control action. This work concludes that BELBIC can be a good choice for positioning of pneumatic actuators.
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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) | 0.001 | 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".