Speed control of SR motor by self-tuning fuzzy PI controller with artificial neural network

Karakaş, Ercüment; Vardarbasi, Soner

doi:10.1007/s12046-007-0044-4

Cited by 18 publications

(10 citation statements)

References 16 publications

(11 reference statements)

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“…Because of this reason, the classic controllers such as P, PI, and PID controllers does not meet the requirements [5]. For SRM controlling, a nonlinear control system must be used [6]. In the last decades, intelligent control techniques are used for nonlinear system and also many studies are done on SRM speed controls by using intelligent techniques [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fuzzy Logic Control of Switched Reluctance Motor

Uysal¹

2017

ijisae

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Abstract:In this study, 8/6 switched reluctance motor (SRM) is controlled by fuzzy logic. For driving SRM, four phase asymmetric bridge converter is chosen. STM32F4 Discovery processor and MATLAB Simulink software fuzzy logic controller (FLC) are used. SRM's speed and current are transferred to the computer in real-time. Measured speeds and currents are plotted. It is shown here that, the SRM for different reference speeds and loads is controlled by a STM32F4 Discovery card with MATLAB Simulink FLC.

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Section: Introductionmentioning

confidence: 99%

Real-Time Fuzzy Logic Control of Switched Reluctance Motor

Uysal¹

2017

ijisae

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“…Many techniques have been illustrated to deal with the speed control of SRM. Fuzzy logic control (FLC) [5][6][7][8][9][10], artificial neural network (ANN) [11,12], robust controller [13][14][15] and adaptive controller [16] have been employed to solve the problem of speed control of SRM. Moreover, optimization techniques like genetic algorithm (GA) [17], particle swarm optimization (PSO) [18][19][20][21][22][23][24][25][26][27][28][29], bacteria foraging [21,22], artificial bee colony [23], firefly [24], imperialist competitive algorithm [25] and BAT algorithm [26] have attracted the attention in designing controller and speed control of various motors.…”

Section: Introductionmentioning

confidence: 99%

Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

Oshaba

Ali

Elazim

2015

Neural Comput & Applic

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This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.List of symbols N r and N s Number of rotor and stator poles, respectively qNumber of phases C rThe commutation ratio b s and b r The stator and rotor pole arc, respectively I and VModule output current and voltage I c and V c Cell output current and voltage I ph and V ph

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“…Fuzzy Logic Control (FLC) [5][6][7][8][9][10], Artificial Neural Network (ANN) [11,12], robust controller [13], and adaptive controller [14] have been employed to solve the problem of speed control of SRM. Moreover, optimization techniques like Genetic Algorithm (GA) [15], Particle Swarm Optimization (PSO) [16][17][18], Bacteria Foraging [19][20][21][22][23] and BAT algorithm [24] have attracted the attention in designing controller and speed control of various motors.…”

Section: Introductionmentioning

confidence: 99%