Controller Design Using Ant Colony Algorithm for a Non-inverting Buck–Boost Chopper Based on a Detailed Average Model

Bozorgi, Amir Masoud; Fereshtehpoor, Vahid; Monfared, Mohammad; Namjoo, Navid

doi:10.1080/15325008.2014.975385

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…The gain optimizer algorithms have been run for maximum number of iterations as 50 and population size has been taken as 10 decided after some trial runs. The IAE is considered as an objective function (J) as per equation (17) and its optimum values are depicted in Table 5. The convergence curves of the objective function over 50 iterations are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Paper [15] reported the grasshopper optimization algorithm for tuning the gain constants of PID controllers. Authors in paper [16] proposed genetic algorithm (GA), PSO and mine blast algorithm (MBA) to tune the gains of PID controllers, whereas in paper [17] GA and ant colony optimization are used for tuning the gain parameters of PI controllers.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Control of Dish-Stirling Solar Thermal Integrated With PMDC Generator Optimized by Meta-Heuristic Approach

Kumar

Sinha

2020

IEEE Access

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This paper proposes an autonomous microgrid system for the first time with solar thermal dish-Stirling engine coupled with permanent magnet direct current generator and battery energy storage system along with uncontrolled variable loads. Both proportional-integral (PI) and proportional-integralderivative (PID) controllers are considered separately for the bidirectional charge controller and buck-boost converter controller for the dish-Stirling system. The modern meta-heuristic algorithms including recent ones like particle swarm optimization, mine blast algorithm and grey wolf optimizer (GWO), have been adopted for tuning the gain parameters of the controllers used for the direct current common bus voltage stability and power sharing. The work considers variable insolation level for somehow mimicking the natural solar variation all the day. The simulation of the proposed model is carried out in MATLAB/Simulink and the results are obtained with two types of controllers for comparison. The results demonstrate the superior performance of the PID controllers optimized with GWO algorithm in terms of faster convergence rate, DC voltage profile, and maintenance of SOC of battery and energy efficiency as compared to the other two algorithms. It is also to be noted that the use of a permanent magnet DC generator coupled to the solar dish-Stirling engine improves the electric conversion efficiency and reduces cost effectively as compared to AC generators. INDEX TERMS DC-DC converter, grey wolf optimizer, microgrid, PI/PID controller, solar dish-stirling, voltage stability.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Control of Dish-Stirling Solar Thermal Integrated With PMDC Generator Optimized by Meta-Heuristic Approach

Kumar

Sinha

2020

IEEE Access

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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: Literature Reviewmentioning

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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Controller Design Using Ant Colony Algorithm for a Non-inverting Buck–Boost Chopper Based on a Detailed Average Model

Cited by 12 publications

References 20 publications

Modeling and Control of Dish-Stirling Solar Thermal Integrated With PMDC Generator Optimized by Meta-Heuristic Approach

Modeling and Control of Dish-Stirling Solar Thermal Integrated With PMDC Generator Optimized by Meta-Heuristic Approach

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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