Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm

Hekimoğlu, Baran; Ekinci, Serdar; Kaya, Serhat

doi:10.1109/idap.2018.8620833

Cited by 42 publications

(32 citation statements)

References 5 publications

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“…Proportional+integral+derivative (PID) controllers are commonly used in industry to improve both the transient and steady state behaviors of a system [1]. These controllers are known as traditional or integer order controllers [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

2019

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In this paper, atom search optimization (ASO) algorithm and a novel chaotic version of it [chaotic ASO (ChASO)] are proposed to determine the optimal parameters of the fractional-order propor-tional+integral+derivative (FOPID) controller for dc motor speed control. The ASO algorithm is simple and easy to implement, which mathematically models and mimics the atomic motion model in nature, and is developed to address a diverse set of optimization problems. The proposed ChASO algorithm, on the other hand, is based on logistic map chaotic sequences, which makes the original algorithm be able to escape from local minima stagnation and improve its convergence rate and resulting precision. First, the proposed ChASO algorithm is applied to six unimodal and multimodal benchmark optimization problems and the results are compared with other algorithms. Second, the proposed ChASO-FOPID, ASO-FOPID, and ASO-PID controllers are compared with GWO-FOPID, GWO-PID, IWO-PID, and SFS-PID controllers using the integral of time multiplied absolute error (ITAE) objective function for a fair comparison. Comparisons were also made for the integral of time multiplied squared error (ITSE) and Zwe-Lee Gaing's (ZLG) objective function as the most commonly used objective functions in the literature. Transient response analysis, frequency response (Bode) analysis, and robustness analysis were all carried out. The simulation results are promising and validate the effectiveness of the proposed approaches. The numerical simulations of the proposed ChASO-FOPID and ASO-FOPID controllers for the dc motor speed control system demonstrated the superior performance of both the chaotic ASO and the original ASO, respectively. INDEX TERMS DC motor speed control, fractional order PID controller, chaotic atom search optimization algorithm, robustness analysis, transient response.

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Section: Introductionmentioning

confidence: 99%

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

2019

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“…In respect, metaheuristic algorithms have so far been played a vital role in terms of designing efficient controllers for different DC-DC power converters. Some of the metaheuristic algorithm examples for efficient control of DC-DC power converters can be listed as genetic algorithm (GA) (Chlaihawi, 2020), chaotic flower pollination algorithm (C ximen et al, 2021), queen-beeassisted GA (Sundareswaran and Sreedevi, 2009), ant colony optimization algorithm (Bozorgi et al, 2015), particle swarm optimization algorithm (Sabanci and Balci, 2020), whale optimization algorithm (WOA) (Hekimog˘lu et al, 2019), cuckoo search algorithm (Mamizadeh et al, 2018), Harris hawks optimization (HHO) algorithm (Ekinci et al, 2019a), differential evolution (Sundareswaran et al, 2014) and artificial fishswarm algorithm (Chanjira and Tunyasrirut, 2020). All those examples have so far demonstrated the greater capability of metaheuristic algorithms in terms of efficient operation of DC-DC power converters.…”

Section: Introductionmentioning

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

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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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“…Several studies on DC motor control are divided into two concepts, namely: conventional methods and artificial intelligence methods. several artificial intelligence concepts for controlling such as: Whale optimization algorithm [8], [9], Harris Hawks optimization algorithm [10], flower pollination algorithm [11], firefly algorithm [12], ant colony algorithm [13], [14], and neural network [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

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Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm

Cited by 42 publications

References 5 publications

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm

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

Improving neural network using a sine tree-seed algorithm for tuning motor DC

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