Genetic algorithm error criteria as applied to PID controller DC-DC buck converter parameters: an investigation

Chlaihawi, Amer A.

doi:10.1088/1757-899x/671/1/012032

Cited by 16 publications

(8 citation statements)

References 13 publications

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“…Thus, to improve the robustness and the efficiency of PIDbased controllers, various methods are used with a ability to improve the dynamical responses of the controller. Numerous control optimizing methods have been presented to increase the stability and fast transient response of the PID controllers, such as Fuzzy-based Logic Control (FLC) strategies [11][12][13] and Artificial Neural Networks (ANN) [14][15][16] methods. Fuzzy logic can add a self-tuning mechanism to the structure providing better disturbance rejection responses .…”

Section: Introductionmentioning

confidence: 99%

Fractional‐order fuzzy PID controller design on buck converter with antlion optimization algorithm

Ghamari

Narm

Mollaee

2021

IET Control Theory & Appl

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The design of a Fractional-Order fuzzy-based PID (FO-F-PID) control structure is presented for Buck converter in the presence of harmful disturbances. A fractional-order proportional-integral-derivative (FO-PID) control scheme is utilized initially to damp the oscillations and remove the steady-state error. To increase the tendency rate of the error to zero, the FO-PID method is applied to a fuzzy-logic-based compensatory stage. At the same time, the fuzzy part gathers the data based on the error and error derivative. The FO-PID control scheme has the capability to enhance the robustness of the control technique against disturbances and parametric variations. Furthermore, to optimize the control parameters, an efficient algorithm so-called Antlion Optimization (ALO) algorithm, is used. Utilizing the ALO algorithm for tuning the FO-PID gains depicts more accurate responses in solving constrained problems with diverse search spaces. Considering numerous disturbances on DC-DC converters, an FO-F-PID controller can be an appropriate alternative since it is more robust against load variations and noise. Moreover, PSO-PID and FO-PSO-PID controllers are designed to drive a comparison between them. Finally, the merits of the presented controller are validated for various scenarios. It can be seen that the FO-F-ALO-PID method provides much better results with faster dynamics. Matlab-Simulink environment is used for the simulations, and the experimental results are tested by the micro-processor to validate the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Fractional‐order fuzzy PID controller design on buck converter with antlion optimization algorithm

Ghamari

Narm

Mollaee

2021

IET Control Theory & Appl

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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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“…Ander et al [16] highlights the role of power electronics in microgrids, especially inverter voltage sources, and introduces a precise method as a precise and proven approach to controlling microgrids. GA-PID controllers were simulated with MATLAB, and the GA optimisation method used to optimise the membership function and gains of the associated PID controller in [17]. The performance of the GA-PID in a DC-DC buck converter was investigated by simulating and analysing maximum overshoot, peak time, and steady state time based on ISE, IAE, IATE and MSE Criteria.…”

Section: Introductionmentioning

confidence: 99%

Performance improvement of decentralized control for bidirectional converters in a DC micro-grid

Abbasi

Nafar

Simab

2021

IJPEDS

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In this paper, using a neural controller and a genetic optimization algorithm to control the voltage as well as, control the frequency of the grid along with the management of the reactive power of the micro-grid to control the output power during islanding using Simultaneous bilateral power converters with voltage/frequency droop strategy and optimization of PI coefficients of parallel power converters by genetic-neural micro-grid algorithm to suppress AC side-current flow that increases stability and improvement of conditions frequency and voltage are discussed. Given the performance of the micro-grid in two simulation scenarios, namely transition from on-grid to off-grid, the occurrence of a step change in load in island mode as well as return to working mode is connected. The ability to detect the robust performance and proper performance of two-level neural controller. The controller performance time was also very good, indicating the appropriate features of the method used to design the controller, namely two-level neural, genetics. The main advantage of this method is its simplicity of design. The method used is also efficient and resistant to changes in the system, which results from the simulations.

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Genetic algorithm error criteria as applied to PID controller DC-DC buck converter parameters: an investigation

Cited by 16 publications

References 13 publications

Fractional‐order fuzzy PID controller design on buck converter with antlion optimization algorithm

Fractional‐order fuzzy PID controller design on buck converter with antlion optimization algorithm

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

Performance improvement of decentralized control for bidirectional converters in a DC micro-grid

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