Intelligent Control Using Metaheuristic Optimization for Buck-Boost Converter

Chanjira, Puchong; Tunyasrirut, Satean

doi:10.1155/2020/5462871

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Based on the proof of Theorem 1 and (9), we continued to calculate the derivative of (17). The derivative of the control u appeared directly on the second-order derivative of the sliding variable s like (9), i.e., ..…”

Section: Design Of Sliding Surfacementioning

confidence: 99%

“…As a result, it is simple and no extra modulation circuits are needed [9,10]. On the other side, SM is inherently nonlinear, which differs from the approximate nonlinearity of the substitutes, like the fuzzy control [11,12], neural network [13,14] and other intelligent control approaches [15][16][17]. As a result, the control performances under SM control are better; besides, the amount and time involved in the calculation can also be saved.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Second-Order Sliding Mode Control of Buck Converters with Multi-Disturbances

et al. 2022

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In this paper, a novel adaptive second-order sliding mode (2-SM) control approach, based on online zero-crossing detection, was proposed to solve the problems of the chattering and fixed control gain for buck converters with multi-disturbances. In modeling, the possible parameter perturbations and external disturbances of the converter system were contained. Instead of the traditional first-order sliding mode (1-SM), the twisting algorithm with 2-SM was adopted for the controller design, which could overcome the chattering problem and realize control continuity. Meanwhile, a novel adaptive mechanism was introduced to replace the conventional fixed control gain by time-varying control gain, the idea of which is to calculate the number of the zero-crossing points of the sliding surface online. As a result, the control magnitude of the improved controller could be reduced to a minimal admissible level, and the steady error of the output voltage could converge to the expected value. Furthermore, the robust stability of the converter system with multi-disturbances wads investigated. Comparative simulations and experiments validated the advantages of this paper as offering better robustness and control performance.

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Section: Design Of Sliding Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Second-Order Sliding Mode Control of Buck Converters with Multi-Disturbances

et al. 2022

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“…In another study on boost converter, a type III controller was used and particle swarm optimization algorithm (Banerjee et al, 2017) was used for efficient tuning. In some other studies, artificial fish-swarm algorithm (Chanjira and Tunyasrirut, 2020) and ant colony optimization algorithm (Bozorgi et al, 2015) were used to tune proportional–integral (PI) controller for efficient operation of buck–boost converter, and the performance of the converter was observed to be improved greatly. A PI controller was also tuned by cuckoo search algorithm (Mamizadeh et al, 2018) in a different study that has achieved good performance for a boost converter.…”

Section: Introductionmentioning

confidence: 99%

A novel-enhanced metaheuristic algorithm for FOPID-controlled and Bode’s ideal transfer function–based buck converter system

İzci

Ekinci

2023

Transactions of the Institute of Measurement and Control

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The search for a better approach to design a convenient controlling scheme is one of the main challenges of the research field related to power converters. This paper brings forward a significant contribution as part of this challenge. In this regard, novel approaches have been proposed for both controller scheme and tuning algorithm. In terms of controlling scheme, a fractional-order proportional–integral–derivative (FOPID) controller was used alongside the Bode’s ideal transfer function to achieve optimum performance of a buck converter system. To achieve a greater performance of the FOPID controller, a novel metaheuristic algorithm, which consists of a balanced structure in terms of explorative and exploitative phases, has also been developed by hybridizing the hunger games search algorithm with simulated annealing technique. The proposed hybrid algorithm helped tuning the FOPID controller optimally. The proposed algorithm and controlling scheme have been proved to be a better approach for controlling a buck converter system in terms of time and frequency domains along with disturbance rejection. To further verify the effectiveness of the proposed hybrid algorithm–based controlling scheme, it was compared with other reported and capable approaches which have also confirmed the greater performance of the proposed approach.

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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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Intelligent Control Using Metaheuristic Optimization for Buck-Boost Converter

Cited by 6 publications

References 14 publications

Adaptive Second-Order Sliding Mode Control of Buck Converters with Multi-Disturbances

Adaptive Second-Order Sliding Mode Control of Buck Converters with Multi-Disturbances

A novel-enhanced metaheuristic algorithm for FOPID-controlled and Bode’s ideal transfer function–based buck converter system

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

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