Intelligent and Robust Controller Tuned with WOA: Applied for the Inverted Pendulum
Bibliographic record
Abstract
Inverted pendulum is a well-known problem in the control theory because several systems such as robot balancing, Segway, hover board riding and operation of a rocket propeller are inherently based on Inverted Pendulum, furthermore it possesses a height non-linear and unstable dynamics. The main objective of our study is to introduce a comparative analysis of fuzzy logic (FLC), radial basis function neural network (RBF) and integral sliding mode control (ISMC) tuned with whale optimizer algorithm (WOA) for the control of the angle position and velocity of the inverted pendulum system. The implemented controller schemas can adequately reflect and approximate a certain type of uncertainties, nevertheless their parameters should be fine-tuned in order to get height and efficient performance, therefore all the antecedents and consequences of those controllers were tuned with WOA. This later provide height accuracy and fast convergence with height dimensional cost function. Comparative results shows that ISMC-WOA outperforms other techniques in term of settling time and overshoot.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".