Particle swarm metaheuristic applied to the optimization of a PID controller

Ramirez, John A.; López-Lezama, Jesús M.; Muñóz-Galeano, Nicolás

doi:10.12988/ces.2018.87350

Cited by 5 publications

(1 citation statement)

References 9 publications

(10 reference statements)

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“…Therefore, since tuning is a search problem, many tuning strategies based on uninformed search algorithms such as Genetic Algorithms (GA) (Febina, Sunil, and Jacob, 2018), Particle Swarm Optimization (PSO) (Ramirez, López, and noz, 2018), and Ant Colony Optimization (ACO) (Jagatheesan, Anand, Dey, and Ashour, 2018) have also been proposed. Other randomized search strategies inspired by collective behaviors observed in nature have also been considered (Ali, Afandi, Parwati, Hidayat, and Hasyim, 2019;Ataslar-Ayyıldız and Karahan, 2018).…”

Section: Conclusionesmentioning

confidence: 99%

PID controller tuning using bacterial Quorum Sensing (QS)

Sarmiento¹,

Soto²

2020

Tecnura

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Objective: PID controllers are widely used to operate AC motors due to their simplicity and easy implementation. However, adjusting its parameters in search of an optimal scheme can be complex because it requires manual tuning by trial and error. This research aims to implement an optimized tuning scheme through a search based on the idealized behavior of a community of bacteria and its Quorum Sensing (QS). Methodology: A closed-loop system model with PID control considering disturbances is proposed in order to tune a disturbance-resistant controller. The response of the model is calculated using a search that mimics a simplified model of bacterial behavior. The scheme uses ITSE (Integral Time Squared Error) as the performance index. Results: The tuning resulting from the proposed scheme was evaluated by simulation and compared with tunings of the same model made by Root Locus and Genetic Algorithms (GA). The results showed a satisfactory response according the design criteria. Conclusions: Nowadays, PID controllers are still basic industrial control tools, particularly important in motor operation. The performance of these controls depends fundamentally on the design of their gain. In the case of complex plants, additional tools are required to facilitate PID tuning. We propose an intelligent and bio-inspired tuning scheme that demonstrates high performance in laboratory tests. Financing: University Francisco José de Caldas through the project 1-72-578-18.

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Section: Conclusionesmentioning

confidence: 99%