Hybrid Firefly and Particle Swarm Optimization Algorithm for PID Controller Design of Buck Converter

Ekinci, Serdar; Hekimoğlu, Baran; Eker, Erdal; Sevim, Davut

doi:10.1109/ismsit.2019.8932733

Cited by 14 publications

(22 citation statements)

References 10 publications

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“…Likewise, the proposed algorithm was shown to have better performance in that respect, as well. (6) The greater performance of the proposed mLFD algorithm-based and Bode's ideal transfer function adopted PIDA controller approach was also demonstrated comparatively using the same buck converter system that was designed by utilizing other published approaches of HHO-based PID (Ekinci et al, 2019a), GA-based PID (Hekimog˘lu et al, 2019), WOA-based PID (Hekimog˘lu et al, 2019), SA-based PID (Hekimog˘lu and Ekinci, 2020), hybrid WOA and SAbased PID (Hekimog˘lu and Ekinci, 2020) and hybrid firefly particle swarm optimization (HFPSO)-based PID (Ekinci et al, 2019b) controllers. All those approaches have gained good results.…”

Section: Contributionsmentioning

confidence: 91%

“…The proposed modified algorithm-based controller design approach was also compared with other published works that have attempted to reach a better design for the same buck converter system. Table 10 lists the available and good performing approaches of HHO-based PID (HHO-PID) controller (Ekinci et al, 2019a), GA-based PID (GA-PID) controller (Hekimog˘lu et al, 2019), WOA-based PID (WOA-PID) controller (Hekimog˘lu et al, 2019), SA-based PID (SA-PID) controller (Hekimog˘lu and Ekinci, 2020), hybrid WOA and SA-based PID (WOASAT-PID) controller (Hekimog˘lu and Ekinci, 2020) and HFPSO-based PID (HFPSO-PID) controller (Ekinci et al, 2019b). The respective table also provides the related controller parameters obtained by the stated approaches.…”

Section: Comparison With the Published Workmentioning

confidence: 99%

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A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system

İzci

Ekinci

Hekimoğlu

2021

Transactions of the Institute of Measurement and Control

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In this paper, an optimal proportional, integral, derivative and acceleration (PIDA) controller design based on Bode’s ideal reference model and a novel modified Lévy flight distribution (mLFD) algorithm is proposed for buck converter system. The modification of the original Lévy flight distribution (LFD) algorithm was achieved by improving exploration and exploitation capabilities of the algorithm through incorporation of opposition-based learning mechanism and hybridizing with simulated annealing algorithm, respectively. The modified algorithm was used to tune the gains of the PIDA controller in order to operate a buck converter system that is mimicking the response of the Bode’s ideal reference model. Both the proposed novel algorithm and its PIDA controller design implementation for buck converter were confirmed through various tests and extensive analyses of statistical and non-parametric tests, convergence profile, transient and frequency responses, disturbance rejection, robustness, and time delay response. The comparative results with the state-of-the-art algorithms of manta ray foraging optimization, arithmetic optimization algorithm and the original LFD algorithm have shown that the proposed mLFD algorithm performs better than the compared ones in all assessments even when different well-known performance indices are used. The proposed Bode’s ideal reference model-based optimal PIDA control design with novel mLFD algorithm was also compared with other design approaches using the same buck converter system available in the literature. The proposed mLFD algorithm-based design approach has also shown greater effectiveness compared to other available methods, as well.

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Section: Contributionsmentioning

confidence: 91%

Section: Comparison With the Published Workmentioning

confidence: 99%

A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system

İzci

Ekinci

Hekimoğlu

2021

Transactions of the Institute of Measurement and Control

Self Cite

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“…A wide range of metaheuristic algorithms have being developed over the last three decades, and many metaheuristics are increasingly becoming popular (Yang, 2011). These metaheuristics techniques have been applied on control systems, and the results obtained shows significant improvement over classical techniques (Agnihotri et al, 2018;Ekinci et al, 2019;Ibrahim & Mahmoud, 2014;Meena & Chahar, 2017;Obeng & Karam, 2018;Sabir & Khan, 2014;Solihin et al, 2011;Taki El-Deen et al, 2015). Abdulameer et al, (2016) designed a PID controller for DC speed motor using the Ziegler-Nichols and Chien-Hrones-Reswick method.…”

Section: Introductionmentioning

confidence: 99%

“…The PSO-PID was successful in reducing the overshoot and settling time of the DC Motor speed response. Ekinci et al, (2019) designed a PID tuned controller for the regulation of the speed of DC motor using the Harris Hawk Optimization (HHO) algorithm. Results obtained shows the HHO PID controller outperforms the atomic search optimization PID and the sine cosine algorithm PID.…”

Section: Introductionmentioning

confidence: 99%

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

Abba¹,

Karataev²,

Thomas³

et al. 2022

FUOYEJET

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This paper presents an optimal PID controller for speed control of DC motor using Smell Agent Optimization (SAO) Algorithm. DC Motor is an energy converter which transforms electrical energy into mechanical energy used in several industrial applications. A SAO based PID is proposed for optimal tuning of the parameters of the controller to improve speed control of the motor. The SAO uses the phenomenon of smell perception and the intuitive trailing behavior of an agent to identify the source of smell. The integral of time multiplied absolute error (ITAE) is taken as the objective function for obtaining the parameters of the PID controller. It has been observed SAO-PID controller outperforms the GA based PID controller with less settling time, rise time and overshoot. Keywords— DC motor, PID controller, rising time, settling time, smell agent optimization algorithm

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“…Firefly algorithm was proposed in (Yang, 2008), is a nature-inspired and stochastic optimization algorithm that has many diverse application areas from engineering to NP-hard (non-deterministic polynomial time) problems. Firefly and particle swarm optimization-based PID controller design for a buck converter is given in (Ekinci et al , 2019). Firefly algorithm-based PI parameter optimization of permanent magnet synchronous motor is presented in (Wang et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Improved PID rate control of a quadrotor with a convexity-based surrogated model

Kaba

2021

AEAT

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Purpose The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm. Design/methodology/approach An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function. Findings Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials. Originality/value The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm.

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Hybrid Firefly and Particle Swarm Optimization Algorithm for PID Controller Design of Buck Converter

Cited by 14 publications

References 10 publications

A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system

A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

Improved PID rate control of a quadrotor with a convexity-based surrogated model

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