Comparative Analysis of Spiral Dynamic Algorithm and Artificial Bee Colony Optimization for Position Control of Flexible Link Manipulators
Bibliographic record
Abstract
Evolutionary algorithms have significantly advanced robotics by enabling the creation of efficient and intelligent robotic systems.This study aims to evaluate the effectiveness of two optimization algorithms, artificial bee colony (ABC) and spiral dynamic algorithm (SDA), in controlling the position of a flexible-link manipulator.By integrating the ABC algorithm into the manipulator's control system, the goal is to enhance its ability to plan paths and optimize trajectories.Additionally, the spiral dynamics algorithm, which draws on principles from complex adaptive systems and human values, provides a framework for modelling system evolution.The study hypothesizes that combining these two algorithms will improve the flexible link manipulator's adaptability and flexibility in dynamic environments and varied task conditions.The results support this hypothesis, demonstrate that the combined ABC and spiral dynamics approach outperforms conventional methods in several key performance metrics, including PID parameter tuning, overshoot, rise time, settling time, and steady-state error.In industry application such a motoring machinery, it is crucial to achieve these metrics at best, which kept the overshoot below 10%, settling time and rise time within a second.Interestingly, the manipulator's behaviours using the spiral dynamics algorithm for PID controller tuning were superior to those using alternative methods.Specifically, the spiral dynamics approach yielded the lowest overshoot at 5.83%, compared to 16.64% with the heuristic method and 8.82% with the ABC method.The SDA has the fastest rise time and settling time which is 0.03267 s and 0.18445 s respectively.Overall, the simulation results indicate that employing these algorithms for PID parameter optimization effectively enhances the manipulator's transient response and minimizes steady-state error, offering promising implications for real-world robotic applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".