Optimization of fuzzy controller design using a Differential Evolution algorithm with dynamic parameter adaptation based on Type-1 and Interval Type-2 fuzzy systems

Ochoa, Patricia; Castillo, Oscar; Soria, José Manuel Nieto

doi:10.1007/s00500-019-04156-3

Cited by 49 publications

(22 citation statements)

References 41 publications

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“…These factors indicate if the algorithm is able to find a near-optimum solution effectively or not. Adopting the right value using trial–error approach takes a large amount of time 52 , 53 . There are several studies on the effect of these parameters on the performance of DE 52 .…”

Section: Methodsmentioning

confidence: 99%

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

Babanezhad

Zabihi

Behroyan

et al. 2021

Sci Rep

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In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.

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Section: Methodsmentioning

confidence: 99%

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

Babanezhad

Zabihi

Behroyan

et al. 2021

Sci Rep

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“…Mutlag et al 34 designed an advanced controller using differential search optimization based FLC to obtain the lowest value of OF and best value of MFs. Ochoa et al 35 deployed Type-1 and Interval Type-2 fuzzy systems to enhance the performance of differential evolution (DE) algorithm to achieve dynamic adaptation of the mutation parameters as well as optimize the MFs. Castillo et al 36 analyzed and compared the FLC optimization algorithms including bee colony optimization (BCO), DE, and harmony search algorithms.…”

Section: Introductionmentioning

confidence: 99%

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

Hannan

Ali²,

Lipu

et al. 2020

Nat Commun

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Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.

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“…It is due to various aspects, like lack of input information, weather and road conditions, etc. In order to tackle uncertainty, fuzzy set theory which was introduced by Zadeh [13] and Bellman and Zadeh [14] could be applied [15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Goal Programming Approach to Multiobjective Transportation Problems

Rivaz

Nasseri

Ziaseraji

2020

Fuzzy Information and Engineering

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The current paper focuses on a multiobjective transportation problem which is a special type of vector minimum problem. In order to deal with such a problem, a new model based on fuzzy goal programming is suggested. Different solutions are obtained according to the priorities of the decision maker in the proposed model. With respect to its properties, at least a weakly efficient solution is yielded by solving the new model. To demonstrate the validity and strengths of the suggested model, some numerical examples are given. Further, a comparison is made with different existing methods.

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Optimization of fuzzy controller design using a Differential Evolution algorithm with dynamic parameter adaptation based on Type-1 and Interval Type-2 fuzzy systems

Cited by 49 publications

References 41 publications

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

A Fuzzy Goal Programming Approach to Multiobjective Transportation Problems

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