Real-Time Implementation of an Enhanced PID Controller Based on Ant Lion Optimizer for Micro-Robotics System
Bibliographic record
Abstract
Microparticles have the potentials to be used for many medical purposes in-side the human body such as drug delivery and other operations. This paper offers a comprehensive comparative study of three meta-heuristic search algorithms for controlling the micro-robotics system with a proportional-integral-derivative (PID) controller. Grey Wolf Optimization (GWO), Harmony Search algorithm (HS) and Ant Lion Optimizer (ALO) are the various techniques that this study adopts. The optimum position control can be obtained by employing the former algorithms with different fitness functions, namely Integral Absolute Error (IAE), Integral of Time Multiplied by Square Error (ITSE), Integral Square Time multiplied square Error (ISTES), Integral Square Error (ISE), Integral of Square Time multiplied by square Error ( (ISTSE), and Integral of Time multiplied by Absolute Error (ITAE). In a MATLAB Simulink, each control method was presented, while the experimental measurements were tested and operated by the LabVIEW Software. It is observed that the HS technique achieves the highest values of settling error for both simulation and experimental results among other control approaches, while the ALO approach reduces the settling error by 32.5% compared to former experiments. The results indicate that ALO is the best method among all approaches and that ISTES is the best choice of PID for optimizing the controlling parameters.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".