Implementation of Genetic Algorithm in Control Structure of Induction Motor A.C. Drive

Brandštetter, Pavel; Dobrovsky, Marek; Kuchař, Martin

doi:10.4316/aece.2014.04003

Cited by 21 publications

(3 citation statements)

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“…What more, the conventional methods used for the identification of systems parameters are based on a number of assumptions that are not valid under all operating conditions. In recent years, the research in the field of electrical drives is has been also focused on the application of different methods of artificial intelligence [1,[3][4][5][6][7]13,15,17] as the theory of artificial neural networks (ANN) and the fuzzy sets theory or the genetic algorithms theory. The theory of fuzzy sets is most often used for systems control [4,7,15] and the genetic algorithms are often used for optimization tasks [5,7] in the field of electric drives.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Application for Mechanical Parameters Identification of Asynchronous Motor

Balara¹,

Timko²,

Zilkova³

et al. 2017

NNW

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This paper describes the first attempt of hardware implementation of Multistream Compression (MSC) algorithm. The algorithm is transformed to series of Finite State Machines with Datapath using Register-Transfer methodology. Those state machines are then implemented in VHDL to selected FPGA platform. The algorithm utilizes a special tree data structure, called MSC tree. For storage purpose of the MSC tree a Left Tree Representation is introduced. Due to parallelism, the algorithm uses multiple port access to SDRAM memory.

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Section: Introductionmentioning

confidence: 99%

Neural Networks Application for Mechanical Parameters Identification of Asynchronous Motor

Balara¹,

Timko²,

Zilkova³

et al. 2017

NNW

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show abstract

“…The simulation results demonstrated the suitability of this method, however, there was still high ripple of the actual speed at high-speed ranges. In [9], authors introduced the method [3].…”

mentioning

confidence: 99%

Current-Sensorless Method for Speed Control of Induction Motor Based on Hysteresis Pulse Width Modulation Technique

Tran

Brandštetter²,

Dinh³

et al. 2018

J. ADV. ENG. COMPUT.

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This paper describes a new solution to control the induction motor drive system without current sensors based on improving the Hysteresis Pulse Width Modulation technique. It has proposed a control scheme which uses stator currents estimated from the differential equation of state variables to replace the feedback signal from current sensors applied in the hysteresis current controller. To verify the proposed method, simulations in MATLAB/Simulink have been implemented in various operating conditions of the motor where its speed has been changed under load as well as no load conditions. The simulation results have demonstrated the effectiveness of the proposed control method for inductive motor drives. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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“…In recent years, the research in the field of electrical drives is has been also focused on the application of different methods of artificial intelligence [1,[3][4][5][6][7]13,15,17] as the theory of artificial neural networks (ANN) and the fuzzy sets theory or the genetic algorithms theory. The theory of fuzzy sets is most often used for systems control [4,7,15] and the genetic algorithms are often used for optimization tasks [5,7] in the field of electric drives. The artificial neural networks (ANNs) are most commonly used for solving tasks as: approximation, association, classification, control or prediction.…”

Section: Introductionmentioning

confidence: 99%

Transition Modes Between Spiking and Bursting in a Pacemaker Neuron

Ye¹,

Liu²,

Liu³

2017

NNW

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A method for identification of mechanical parameters of an asynchronous motor is presented in this paper. The identification method is based on the use of our knowledge of the system. This paper clarifies the method by using the example identifying of mechanical parameters of the three-phase squirrel-cage asynchronous motor.A model of mechanical subsystem of the motor is presented as well as results of simulation. The special neural network is used as an identification model and its adaptation is based on the gradient descent method.The parameters of mechanical subsystem are derived from the values of synaptic weights of the neural identification model after its adaptation. Deviation of identified mechanical parameters in the case of moment inertia was up to 0.03 % and in the case of load torque was 1.45 % of real values.

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Implementation of Genetic Algorithm in Control Structure of Induction Motor A.C. Drive

Cited by 21 publications

References 0 publications

Neural Networks Application for Mechanical Parameters Identification of Asynchronous Motor

Neural Networks Application for Mechanical Parameters Identification of Asynchronous Motor

Current-Sensorless Method for Speed Control of Induction Motor Based on Hysteresis Pulse Width Modulation Technique

Transition Modes Between Spiking and Bursting in a Pacemaker Neuron

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