Comparative Performance Evaluation of Swarm Intelligence-Based FOPID Controllers for PMSM Speed Control
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Bibliographic record
Abstract
This paper presents a study focused on the design and performance evaluation of Fractional-Order Proportional-Integral-Derivative (FOPID) controllers for the speed control of Permanent Magnet Synchronous Motors (PMSMs).Effective speed control of PMSMs is of great importance in various applications such as robotics, electric vehicles, and industrial automation.However, achieving precise and efficient speed control poses several challenges due to the nonlinear and time-varying nature of PMSMs.To address these challenges, the study proposes the utilization of FOPID controllers, which offer advantages over traditional PID controllers, including improved robustness and greater flexibility in handling complex system dynamics.Additionally, the study explores the use of Swarm Intelligence (S.I.) algorithms for the design and tuning of FOPID controllers.Swarm Intelligence algorithms, such as Particle Swarm Optimization (PSO), ant colony optimization (ACO), and Grey Wolf Optimization (GWO), are known for their ability to effectively search and optimize complex parameter spaces.The main contribution of this work is the comparison and evaluation of PSO, GWO, and ACO algorithms for the design of FOPID controllers in PMSM speed control applications.The controllers are assessed through both simulations and experimental tests to analyze their performance in terms of speed-tracking accuracy, overshoot, and settling time.The key finding of the study is that the ACO-FOPID controller exhibits the best performance in terms of transient response.It achieves a rise time of 0.008978 s, a settling time of 0.01 s, and zero absolute time error (ITAE).These results indicate that the ACO-FOPID controller provides precise and fast speed control for PMSMs, making it a promising solution for practical applications.In summary, this study highlights the importance of PMSM speed control and the challenges associated with it.It introduces the FOPID controller as a potential solution and motivates the utilization of Swarm Intelligence algorithms for its design.The comparison of PSO, GWO, and ACO algorithms for FOPID controller design demonstrates the superiority of the ACO-FOPID controller in terms of transient response.This research contributes to the advancement of control systems for PMSMs and showcases the potential of Swarm Intelligence algorithms in optimizing complex control parameters.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 it