Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Patel, Himanshukumar R.; Shah, Viral B.

doi:10.1108/ijicc-01-2022-0011

Cited by 11 publications

(3 citation statements)

References 44 publications

(77 reference statements)

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“…Additionally, statistical analysis and a Friedman rank test confirm the superiority of the JOA algorithm-based PID controller over other controllers. In future, fractional order controllers 26,27 , fuzzy controllers [28][29][30][31][32][33] and learning based controllers 34,35 , along with learning based optimization techniques 36 , can be implemented for optimal generation control. Z 0102 0.00447 0.00426 0.00364 0.00336 0.00425 0.00346…”

Section: Discussionmentioning

confidence: 99%

Investigation of rank order centroid method for optimal generation control

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

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Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher–learner based optimization algorithm (TLBOA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller’s performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

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Section: Discussionmentioning

confidence: 99%

Investigation of rank order centroid method for optimal generation control

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

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“…Some relevant works have been studied, for example; in [25] an optimization of the T2FL is presented with the hybridization of GSO and FA algorithms, and in [26], a shadowed type-2 FS in the improvement of the cuckoo search and flower pollination algorithms is presented. In [27], a type-2 FL is implemented in the improvement of a metaheuristic algorithm, and in [28], a bat algorithm is implemented in the optimization of an IT2FLS. In [29], a metaheuristic approach for IT2FLS is implemented, and in [30], a type-2 FS is implemented in the dynamic adaptation of harmony search optimization [31] and bee colony optimization [32].…”

Section: Related Workmentioning

confidence: 99%

An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

Amador-Angulo,

Castillo

2023

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In this paper, an improved whale optimization algorithm (WOA) based on the utilization of an interval type-2 fuzzy logic system (IT2FLS) is presented. The main idea is to present a proposal for adjusting the values of the r→1 and r→2 parameters in the WOA using an IT2FLS to achieve excellent results in the execution of the WOA. The original WOA has already proven itself as an algorithm with excellent results; therefore, a wide variety of improvements have been made to it. Herein, the main purpose is to provide a hybridization of the WOA algorithm employing fuzzy logic to find the appropriate values of the r→1 and r→2 parameters that can optimize the mathematical functions used in this study, thereby providing an improvement to the original WOA algorithm. The performance of the fuzzy WOA using IT2FLS (FWOA-IT2FLS) shows good results in the case study of the benchmark function optimization. An important comparative with other metaheuristics is also presented. A statistical test and the comparative with other bio-inspired algorithms, namely, the original WOA with type-1 FLS (FWOA-T1FLS) are analyzed. The performance index used is the average of the minimum errors in each proposed method.

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“…This type of solution was described by Kia et al 15 . The advantage of systems based on fuzzy logic is that they can easily use human knowledge 16 , 17 —e.g. experts in a given field.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy logic control for watering system

Neugebauer,

Akdeniz,

Demir

et al. 2023

Sci Rep

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A two-dimensional finite element (FEM) model was developed to simulate water propagation in soil during irrigation. The first dimension was water distribution depth in soil, and the second dimension was time. The developed model was tested by analyzing water distribution in a conventional (clock-controlled) irrigation model. The values in the conventional model were calculated based on the literature. The results were consistent with the results obtained from the model. In the next step, a fuzzy logic model for irrigation control was developed. The input variables were ambient temperature, soil moisture content and time of day (which is related to solar radiation and evapotranspiration), and the output variable was irrigation intensity. The fuzzy logic control (FLC) model was tested by simulating water distribution in soil and comparing water consumption in both models. The study demonstrated that the depth of the soil moisture sensor affected water use in the fuzzy logic-controlled irrigation system relative to the conventional model. Water consumption was reduced by around 12% when the soil moisture sensor was positioned at an optimal depth, but it increased by around 20% when sensor depth was not optimal. The extent to which the distribution of fuzzy variables affects irrigation performance was examined, and the analysis revealed that inadequate distribution of fuzzy variables in the irrigation control system can increase total water consumption by up to 38% relative to the conventional model. It can be concluded that a fuzzy logic-controlled irrigation system can reduce water consumption, but the system’s operating parameters should be always selected based on an analysis of local conditions to avoid an unintended increase in water use. A well-designed FLC can decrease water use in agriculture (thus contributing to rational management of scarce water resources), decrease energy consumption, and reduce the risk of crop pollution with contaminated groundwater.

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Type-2 fuzzy logic applications designed for active parameter adaptation in metaheuristic algorithm for fuzzy fault-tolerant controller

Cited by 11 publications

References 44 publications

Investigation of rank order centroid method for optimal generation control

Investigation of rank order centroid method for optimal generation control

An Interval Type-2 Fuzzy Logic Approach for Dynamic Parameter Adaptation in a Whale Optimization Algorithm Applied to Mathematical Functions

Fuzzy logic control for watering system

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