Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

Borni, Abdelhalim; Talbi, Abdelkrim; Zaghba, Layachi; Bouchakour, Abdelhak; Lakhdari, Abdelkader; Zarour, Laid

doi:10.1063/1.4976225

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Basically, the PSO algorithm has been used to find an optimum search space in complex areas by interacting with people in a particle population [57]. A number of problems such as artificial neural network training [58], fuzzy logic control [59], or pattern classification [60] have been successfully addressed.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Seismic Model Parameter Optimization for Building Structures

Lengyel

Stan

Miclea

2020

Sensors

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Structural dynamic modeling is a key element in the analysis of building behavior for different environmental factors. Having this in mind, the authors propose a simple nonlinear model for studying the behavior of buildings in the case of earthquakes. Structural analysis is a key component of seismic design and evaluation. It began more than 100 years ago when seismic regulations adopted static analyzes with lateral loads of about 10% of the weight of the structure. Due to the dynamics and non-linear response of the structures, advanced analytical procedures were implemented over time. The authors’ approach is the following: having a nonlinear dynamic model (in this case, a multi-segment inverted pendulum on a cart with mass-spring-damper rotational joints) and at least two datasets of a building, the parameters of the building’s model are estimated using optimization algorithms: Particle Swarm Optimization (PSO) and Differential Evolution (DE). Not having much expertise on structural modeling, the present paper is focused on two aspects: the proposed model’s performance and the optimization algorithms performance. Results show that among these algorithms, the DE algorithm outperformed its counterpart in most situations. As for the model, the results show us that it performs well in prediction scenarios.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Seismic Model Parameter Optimization for Building Structures

Lengyel

Stan

Miclea

2020

Sensors

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“…Wind energy conversion systems (WECSs) have lately aroused interest of many academics [1]. Wind power contributes to the long-term maintenance of biodiversity in natural areas because of low hazard (100 percent natural) and large economic benefits (renewable and sustainable electricity).…”

Section: Introductionmentioning

confidence: 99%

“…Because solar systems (PVS) take up much space for a small amount of power, wind energy has the advantage of a greater output in the winter (the wind is generally stronger in the cold season). Due to greater energy consumption in the winter, the living expenses are higher [1,2], so this might become a significant benefit.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of P&O and Fuzzy MPPT Controllers and Their Optimization Using PSO and GA to Improve Wind Energy System

2021

IJEM

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“…Paper (Juang and Chang, 2010) proposed the design of fuzzy rule-based systems using ant-colony optimization (ACO), which determines the number of fuzzy rules and optimizes all the parameters in each fuzzy rule. Paper (Prado et al, 2010;Ayu Purnama and Muslim, 2017;Borni et al, 2017;Taieb et al, 2018) introduced a swarm-intelligence approach based on the use of particle-swarm optimization (PSO) to obtain the antecedents, consequences and connectives of the fuzzy rules. Paper (Berrazouane and Mohammedi, 2014;Gadekallu and Khare, 2017;Ding et al, 2015) presented the development of an optimized fuzzy logic controller for operating a standalone hybrid power system based on cuckoo search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Uncertain nonlinear system control using hybrid fuzzy LQR-sliding mode technique optimized with evolutionary algorithm

Sơn

Kien

Ánh

2019

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Purpose This paper aims to propose an advanced tracking control of the uncertain nonlinear dynamic system using a novel hybrid fuzzy linear quadratic regulator (LQR)-proportional-integral-derivative (PID) sliding mode control (SMC) optimized by differential evolution (DE) algorithm. Design/methodology/approach First, a swing-up and balancing control is presented for an experimental uncertain nonlinear Pendubot system perturbed with friction. The DE-based optimal SMC scheme is used to optimally swing up the Pendubot system to the top equilibrium position. Then the novel hybrid fuzzy-based on LQR fusion function and PID controller optimized by DE algorithm is innovatively applied for balancing and control the position of the first link of the Pendubot in the down-right position with tracking sinusoidal signal reference. Findings Experimental results demonstrate the robustness and effectiveness of the proposed approach in balancing control for an uncertain nonlinear Pendubot system perturbed with internal friction. Originality/value This manuscript is an original research paper and has never been submitted to any other journal.

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Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

Cited by 13 publications

References 17 publications

Seismic Model Parameter Optimization for Building Structures

Seismic Model Parameter Optimization for Building Structures

Comparative Study of P&O and Fuzzy MPPT Controllers and Their Optimization Using PSO and GA to Improve Wind Energy System

Uncertain nonlinear system control using hybrid fuzzy LQR-sliding mode technique optimized with evolutionary algorithm

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