A Novel Integration of Metaheuristic – Based Optimization Methods for Enhancing Fuzzy Logic Control Performance on Inverted Pendulum-Cart Systems
Bibliographic record
Abstract
This paper proposes a novel hybrid control strategy for improving the balance and trajectory tracking performance of a typical inverted pendulum system.The system is made up of a freely circling pendulum mounted on a horizontally mobile cart.The control objective is to stabilize the pendulum in an upright position while simultaneously guiding the cart along a desired trajectory.A hybrid optimization approach, combining an enhanced Particle Swarm Optimization (PSO) algorithm with the BA Algorithm (BA), is proposed to optimize the critical parameters of a direct fuzzy logic controller.In the initialization phase, PSO is utilized to generate a high-quality initial population.Subsequently, BA refines the optimization by tuning the scaling factors of the fuzzy controller.The direct fuzzy controller incorporates five preprocessing and postprocessing factors, which significantly impact the overall control performance.Numerical simulations and experimental results demonstrate that the proposed PSO-BA hybrid method achieves faster computation times and efficiently identifies optimal parameters, resulting in rapid and robust control responses even in large search spaces.Comparative analysis reveals that this novel approach outperforms conventional PID controllers and fuzzy controllers optimized with standard PSO-based techniques, exhibiting superior control quality and responsiveness.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".