Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial Applications Article Swipe
R. S. Patil
,
Sharad P. Jadhav
,
Mukesh D. Patil
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18196/jrc.v5i2.20850
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18196/jrc.v5i2.20850
PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. Keywords— PID controller; Intelligent and Nature-Inspired Algorithms; Fuzzy Logic; Artificial Neural Network; Adaptive NeuroFuzzy Inference System; Genetic Algorithm; Particle Swarm Optimization; Differential Evolution; Ant Colony Optimization; Simulated Annealing; Artificial Bee Colony; Firefly Algorithm; Cuckoo Search; Harmony Search; Grey Wolf Optimization.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18196/jrc.v5i2.20850
- https://journal.umy.ac.id/index.php/jrc/article/download/20850/8978
- OA Status
- diamond
- Cited By
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410312595
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410312595Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18196/jrc.v5i2.20850Digital Object Identifier
- Title
-
Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial ApplicationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-12Full publication date if available
- Authors
-
R. S. Patil, Sharad P. Jadhav, Mukesh D. PatilList of authors in order
- Landing page
-
https://doi.org/10.18196/jrc.v5i2.20850Publisher landing page
- PDF URL
-
https://journal.umy.ac.id/index.php/jrc/article/download/20850/8978Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://journal.umy.ac.id/index.php/jrc/article/download/20850/8978Direct OA link when available
- Concepts
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PID controller, Computer science, Control engineering, Control theory (sociology), Algorithm, Artificial intelligence, Engineering, Control (management), Temperature controlTop concepts (fields/topics) attached by OpenAlex
- Cited by
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24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 22, 2024: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.conventional | 96 |
| abstract_inverted_index.evolutionary | 55 |
| abstract_inverted_index.quantitative | 220 |
| abstract_inverted_index.researchers. | 207 |
| abstract_inverted_index.Additionally, | 51 |
| abstract_inverted_index.Optimization. | 288 |
| abstract_inverted_index.Optimization; | 269, 274 |
| abstract_inverted_index.applications, | 26, 177 |
| abstract_inverted_index.applications. | 126, 248 |
| abstract_inverted_index.comprehensive | 17, 145, 181 |
| abstract_inverted_index.disturbances. | 12 |
| abstract_inverted_index.effectiveness | 139 |
| abstract_inverted_index.practitioners | 205 |
| abstract_inverted_index.understanding | 182 |
| abstract_inverted_index.Nature-Inspired | 254 |
| abstract_inverted_index.nature-inspired | 30, 52, 116, 241 |
| abstract_inverted_index.algorithms-based | 242 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.9894976 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |