Robust Takagi Sugeno Fuzzy Models control for a Variable Speed Wind Turbine Based a DFI-Generator

Hemeyine, Ahmed Vall; Abbou, Ahmed; Tidjani, Naoual; Mokhlis, Mohcine; Bakouri, Anass

doi:10.22266/ijies2020.0630.09

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…Compared to the existing control technique, the introduced Takagy-Sugeno control technique improved the performance of the the electrorheological damper partly active chair suspension system. In [18], T-SFMs are applied in the robust control of non-constant speed wind turbines which utilizes a generator of twice-fed induction type. The suggested Takagy-Sugeno control method provides the optimum power under considering non-constant wind speed.…”

Section: Related Workmentioning

confidence: 99%

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Malhotra¹,

Bala²

2022

Computers, Materials &Amp; Continua

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Robotic manipulators are widely used in applications that require fast and precise motion. Such devices, however, are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part. To address these issues, the Linear Matrix Inequalities (LMIs) and Parallel Distributed Compensation (PDC) approaches are implemented in the Takagy-Sugeno Fuzzy Model (T-SFM). We propose the following methodology; initially, the state space equations of the nonlinear manipulator model are derived. Next, a Takagy-Sugeno Fuzzy Model (T-SFM) technique is used for linearizing the state space equations of the nonlinear manipulator. The T-SFM controller is developed using the Parallel Distributed Compensation (PDC) method. The prime concept of the designed controller is to compensate for all the fuzzy rules. Furthermore, the Linear Matrix Inequalities (LMIs) are applied to generate adequate cases to ensure stability and control. Convex programming methods are applied to solve the developed LMIs problems. Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.

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Section: Related Workmentioning

confidence: 99%

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Malhotra¹,

Bala²

2022

Computers, Materials &Amp; Continua

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“…However, to solve tracking trajectory and stabilization problems, and to adapt for parameter variations of the DFIG-based WECS, many controllers have been developed in nonlinear form, such as the sliding mode controller, the backstepping sliding mode controller (BSMC), and the fuzzy logic controller [9].…”

Section: Modeling Of Dfigmentioning

confidence: 99%

“…The sliding mode controller is most studied in the literature [9,10]. It is a robust nonlinear controller that can reject uncertainty and perturbation, but the major drawback of the sliding mode controller is the chattering phenomena.…”

Section: Modeling Of Dfigmentioning

confidence: 99%

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A Robust Interval Type-2 Fuzzy Logic Controller for Variable Speed Wind Turbines Based on a Doubly Fed Induction Generator

et al. 2021

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This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a system could be reduced by using type-2 fuzzy logic since it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. A new concept based on an interval type-2 fuzzy logic controller (IT-2 FLC) was developed because of its uncertainty management capabilities. Both these control strategies were designed and their performances compared for the purpose of showing the control most efficient in terms of reference tracking and robustness. We made a comparison between the performance of the type-1 fuzzy logic controller (T1-FLC) and interval type-2 fuzzy logic controller (IT2-FLC). The simulation results clearly manifest the height robustness of the interval type-2 fuzzy logic controller in comparison to the T1-FLC in terms of rise time, settling time, and overshoot value. The simulations were realized by MATLAB/Simulink software.

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“…Rapid occurrence of global warming and decreasing energy reserves has become a global issue and a challenge for researchers. One of many means of preservation is to develop renewable energy sources that are environmentally friendly, such as solar cell clustering, wind farm optimization, and wind turbine control optimization by incorporating modern technology to improve the efficiency of renewable energy [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Smart Variable Rotor of Vertical Axis Wind Turbine with Faster Cut-in Speed and Wide Range Extract Power Using Artificial Intelligent

Fadil¹,

Soedibyo²,

Ashari³

2020

IJIES

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This paper presents the standalone type vertical axis wind turbine (VAWT) smart rotor using variable diameter rotor (VDR) in order to tap constant power and maintain cut-in wind speed. VDR is a smart variable rotor capable of operating at a low wind speed, in which the width of diameter rotor is adjustable using actuators. The VDR rotor is connected to the permanent magnet synchronous generator (PMSG) and a DC-DC boost converter. The controller of VDR rotors uses fuzzy logic controller (FLC). The FLC variable inputs are wind speed data and power output, broken down into cluster groups to determine the diameter rotor position. The wind speed data as fuzzy input are produced by wind speed estimator using artificial neural network (ANN) to maintain cut-in speed to be faster. The velocity movement of VDR is limited from 75 cm to 150 cm. The VDR extension increases by 25% when the wind speed decreases from 8 to 6 m/s to obtain consistent power at 150 Watt. The experimental of VDR system is tested at low wind speeds ranging from 1 to 8 m/s as a verification of the control system. The result showed that the VDR produced five times increase in efficiency with faster cut-in wind speed at 2.0 m/s.

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Robust Takagi Sugeno Fuzzy Models control for a Variable Speed Wind Turbine Based a DFI-Generator

Cited by 9 publications

References 11 publications

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

A Robust Interval Type-2 Fuzzy Logic Controller for Variable Speed Wind Turbines Based on a Doubly Fed Induction Generator

Smart Variable Rotor of Vertical Axis Wind Turbine with Faster Cut-in Speed and Wide Range Extract Power Using Artificial Intelligent

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