Practical implementation for stable adaptive interval A2-C0 type-2 TSK fuzzy controller

El-Nagar, Ahmad M.

doi:10.1007/s00500-018-3523-0

Cited by 8 publications

(5 citation statements)

References 44 publications

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“…The consequent parts of an IT2‐TSK‐FLS and Mamdani IT2FLS consist of linear functions and membership function, respectively 43,44 . The IT2‐TSK‐FLS is divided into three different models based on the consequent of the type of rule: A2‐C0, A2‐C1, and A2‐C2 45 . The TSK rule is called an A2‐C0 case if the antecedents are IT2FLSs and its consequents are crisp numbers.…”

Section: Design Of Proposed Control Structurementioning

confidence: 99%

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Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

Ganı

Şekkeli

2022

Circuit Theory & Apps

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The conventional angle of incremental conductance (AIC) method fails to track the maximum power point (MPP) when a rapid change in solar irradiation and/or panel temperature appears because it cannot distinguish between rapid changes in photovoltaic (PV) current and voltage under real environmental conditions. The present study describes setbacks of the conventional AIC method and proposes an effective approach based on the combination of AIC and type‐2 fuzzy. The proposed approach offers a significant advantage in terms of maximum power transfer to the load under long‐term weather patterns under which broken clouds appear frequently during a 40‐m testing period. The proposed approach is installed on 250 W module of a 10 kWp real PV plant in Kahramanmaras Sutcu Imam University. Later, its performance is validated by dSPACE1104 controller card operating in real time with MATLAB/Simulink tools using daily measured data. The experimental results obtained from the laboratory setup based on dSPACE1104 controller card demonstrate the facility and feasibility of the proposed approach for real PV plant with boost power converter and load.

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Section: Design Of Proposed Control Structurementioning

confidence: 99%

“…43,44 The IT2-TSK-FLS is divided into three different models based on the consequent of the type of rule: A2-C0, A2-C1, and A2-C2. 45 The TSK rule is called an A2-C0 case if the antecedents are IT2FLSs and its consequents are crisp numbers.…”

Section: Design Of Proposed Control Structurementioning

confidence: 99%

Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

Ganı

Şekkeli

2022

Circuit Theory & Apps

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“…Subsequently, it is essential to develop robust control schemes for solving such problems and to obtain certain performance requirements [1][2][3]. In recent years, robust adaptive schemes based on trajectory tracking have attracted great attention for nonlinear systems such as adaptive probabilistic Takagi-Sugeno-Kang (TSK) fuzzy controller [4], adaptive interval type-2 TSK fuzzy controller [5], adaptive sliding mode control (SMC) [6] and SMC based on proportional-integral-derivative (PID) and proportional-integral (PI) sliding surface, respectively [7,8].…”

Section: Introductionmentioning

confidence: 99%

Development of sliding mode control based on diagonal recurrent neural network for coupled tank system

El-Nagar,

Abdo

2024

Neural Comput & Applic

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This study presents the development of sliding mode control (SMC) using the diagonal recurrent neural network (DRNN) for nonlinear systems. Firstly, the SMC for linear systems is developed for nonlinear coupled tank system. Second, the DRNN is used to design the equivalent part of the SMC law, which is performed to approximate the dynamics of a controlled process. Third, the sliding surface for the switching control is developed using the DRNN. The DRNN parameters are tuned using Lyapunov function to achieve the controlled process stability. For the developed scheme, discontinuous signum function is used to compensate the chattering phenomenon. The developed scheme is applied for controlling the uncertain nonlinear coupled tank system. The simulation results indicate that the developed scheme can respond to the effects of system uncertainties compared to other existing schemes.

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“…Nevertheless, controller design for IT2‐TSK‐FLCs is still a challenging problem because of difficulty in determining and designing the scaling factor, membership function parameters, and rule base, that is, the control surface. The proper selection of these parameters has a remarkable impact on controller performance 13,14 . Trial and error procedures or optimization methods based on meta‐heuristic algorithms can be used to determine the IT2‐TSK‐FLC parameters 15,16 .…”

Section: Introductionmentioning

confidence: 99%

“…The proper selection of these parameters has a remarkable impact on controller performance. 13,14 Trial and error procedures or optimization methods based on meta-heuristic algorithms can be used to determine the IT2-TSK-FLC parameters. 15,16 Among the meta-heuristic optimization algorithms, the firefly algorithm (FA) that was introduced by Yang 17 is used for many engineering problems because of its higher speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Keçecioğlu

2022

Int J Numerical Modelling

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The efficiency is a very important parameter for photovoltaic systems. Positive output elementary super lift Luo converter classified as high gain DC‐DC converter are used in photovoltaic systems because of their advantages such as high efficiency, reduced ripples, and low switching loss. The drawback of the photovoltaic system based on this converter is its sensitivity to small changes on the control signal. Both maximum power point tracking and control signal stability require a powerful control system as this is a challenging task for the photovoltaic system. Interval type‐2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties better than type‐1 fuzzy logic systems. In this work, interval type‐2 Takagi–Sugeno–Kang fuzzy logic controller was first designed as a controller. Then, the controller, which was proposed for this paper, was optimized with the Firefly Algorithm to improve the control performance. Performance of controllers is evaluated via comparative simulation studies under the most demanding environmental conditions. It has been observed that the proposed controller reduces the settling time by more than 50% and increases the efficiency up to 10% according to the simulation situations.

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Practical implementation for stable adaptive interval A2-C0 type-2 TSK fuzzy controller

Cited by 8 publications

References 44 publications

Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

Experimental evaluation of type‐2 fuzzy logic controller adapted to real environmental conditions for maximum power point tracking of solar energy systems

Development of sliding mode control based on diagonal recurrent neural network for coupled tank system

Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

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