Optimal Design and Simulation for PID Controller Using Fractional-Order Fish Migration Optimization Algorithm
Open accessAbstract
Proportional Integral Derivative (PID) controller is one of the most classical controllers, which has a good performance in industrial applications. The traditional PID parameter tuning relies on experience, however, the intelligent algorithm is used to optimize the controller, which makes it more convenient. Fish Migration Optimization (FMO) is an excellent algorithm that mimics the swim and migration behaviors of fish biology. Especially, the formulas for optimization were obtained from biologists. However, the optimization effect of FMO for PID control is not prominent, since it is easy to skip the optimal solution with integer-order velocity. In order to improve the optimization performance of FMO, Fractional-Order Fish Migration Optimization (FOFMO) is proposed based on fractional calculus (FC) theory. In FOFMO, the velocity and position are updated in fractional-order forms. In addition, the fishes should migration back to a position which is more conducive to survival. Therefore, a new strategy based on the global best solution to generate new positions of offsprings is proposed. The experiments are performed on benchmark functions and PID controller. The results show that FOFMO is superior to the original FMO, and the PID controller tuned by FOFMO is more robust and has better performance than other contrast algorithms.
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Conventional controllers cannot deal with system uncertainties and do not give high performance due to their fixed parameters.
“Conventional controllers cannot deal with the system uncertainties and do not give high performance due to their fixed parameters.”
From Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system (2025) · cites this paper as [2]
The Introduction says traditional PID algorithms have low efficiency and no prominent effect, supporting the performance concern. It does not attribute that limitation to fixed parameters; it instead describes PID controllers as having good robustness. The Robustness Analysis compares tuning methods under an added time delay, but does not say conventional controllers cannot handle system uncertainties. Page numbers are unavailable in our copy. (LLM, conf=0.84)
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Sorted by citations
Deep reinforcement learning-based adaptive fuzzy control for electro-hydraulic servo system
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“Therefore, the conventional controllers cannot handle the system uncertainties and perform poorly [2].”
- Authors
- Guo, B., Zhuang, Z., Pan, J.-S., & Chu, S.-C.
- Affiliations
- Shandong University of Science and Technology (CN)
- Flinders University (AU)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 8808-8819
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3120024282
- Cited by
- 59
- References
- 65
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 14 · 2023: 14 · 2022: 15 · 2021: 3
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Design
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Keywords
- PID controller tuning, fractional calculus, metaheuristic optimization, benchmark function optimization, controller robustness
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