A Summary of PID Control Algorithms Based on AI-Enabled Embedded Systems

Proportional-integral-derivative (PID) controllers are extensively used in engineering practices for their simple structures, robustness to model errors, and easy operations. At present, there is a great variety of PID controllers. Companies have developed intelligent regulators with functions for a...

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Veröffentlicht in:Security and communication networks Jg. 2022; S. 1 - 7
1. Verfasser: Zhou, Yi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Hindawi 23.04.2022
John Wiley & Sons, Inc
Schlagworte:
Adaptive control
Ant colony optimization
Artificial intelligence
Control algorithms
Controllers
Embedded systems
Fuzzy control
Fuzzy logic
Fuzzy sets
Genetic algorithms
Mathematical models
Neural networks
Parameter estimation
Predictive control
Principles
Process controls
Proportional integral derivative
Systems stability
ISSN:1939-0114, 1939-0122
Online-Zugang:Volltext
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Zusammenfassung:Proportional-integral-derivative (PID) controllers are extensively used in engineering practices for their simple structures, robustness to model errors, and easy operations. At present, there is a great variety of PID controllers. Companies have developed intelligent regulators with functions for automatically tuning PID parameters. For present PID controllers, strategies such as intelligence, self-adaptation, and self-correction are extended to transmission PID. PID controllers and corresponding improved ones are utilized in 90% of industrial control processes. In this paper, PID control algorithms are summarized. This paper focuses on advanced control strategies such as PID control, predictive PID control, adaptive PID control, fuzzy PID control, neural network PID control, expert intelligent PID control, PID control based on genetic algorithms, and PID control based on ant colony algorithms. Besides, these kinds of algorithms are compared, and prospects of PID algorithms are forecast at the end of this paper.
Bibliographie:ObjectType-Article-1
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ISSN:1939-0114
1939-0122
DOI:10.1155/2022/7156713