Optimized Torque Control via Backstepping Using Genetic Algorithm of Induction Motor

Conventional direct torque control(DTC) of induction motor has the problem of large torque ripple.In addition,the speed sensor has its deficiency.A novel DTC system based on multiple neural networks optimized by Genetic Algorithm is proposed and the structures of the proposed system are designed.Genetic algorithm was used to optimize the initial weights and thresholds of the neural networks,All parameters of the neural networks were obtained by offline training.A simulation model of induction motor DTC system was developed in Matlab／Simulink,the simulation results show the feasibility and effectiveness of the scheme

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High-performance adaptive intelligent Direct Torque Control schemes for induction motor drives

Serbian Journal of Electrical Engineering ◽

10.2298/sjee0501093v ◽

2005 ◽

Vol 2 (1) ◽

pp. 93-116 ◽

Cited By ~ 12

Author(s):

M. Vasudevan ◽

R. Arumugam ◽

S. Paramasivam

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Induction Motor ◽

High Performance ◽

Fuzzy Controller ◽

Linear Time ◽

Direct Torque Control ◽

Torque Control ◽

Advantages And Disadvantages ◽

State Selection

This paper presents a detailed comparison between viable adaptive intelligent torque control strategies of induction motor, emphasizing advantages and disadvantages. The scope of this paper is to choose an adaptive intelligent controller for induction motor drive proposed for high performance applications. Induction motors are characterized by complex, highly non-linear, time varying dynamics, inaccessibility of some states and output for measurements and hence can be considered as a challenging engineering problem. The advent of torque and flux control techniques have partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Intelligent controllers are considered as potential candidates for such an application. In this paper, the performance of the various sensor less intelligent Direct Torque Control (DTC) techniques of Induction motor such as neural network, fuzzy and genetic algorithm based torque controllers are evaluated. Adaptive intelligent techniques are applied to achieve high performance decoupled flux and torque control. This paper contributes: i) Development of Neural network algorithm for state selection in DTC; ii) Development of new algorithm for state selection using Genetic algorithm principle; and iii) Development of Fuzzy based DTC. Simulations have been performed using the trained state selector neural network instead of conventional DTC and Fuzzy controller instead of conventional DTC controller. The results show agreement with those of the conventional DTC.

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Weighting Factors Optimization of Predictive Torque Control of Induction Motor by Multiobjective Genetic Algorithm

IEEE Transactions on Power Electronics ◽

10.1109/tpel.2018.2834304 ◽

2019 ◽

Vol 34 (7) ◽

pp. 6628-6638 ◽

Cited By ~ 15

Author(s):

Paulo Roberto Ubaldo Guazzelli ◽

William Cesar de Andrade Pereira ◽

Carlos Matheus Rodrigues de Oliveira ◽

Allan Gregori de Castro ◽

Manoel Luis de Aguiar

Keyword(s):

Genetic Algorithm ◽

Induction Motor ◽

Torque Control ◽

Multiobjective Genetic Algorithm ◽

Weighting Factors

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Genetic Algorithm based Direct Torque Control of VSI fed Induction Motor Drive using MATLAB Simulation

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2019/76852019 ◽

2019 ◽

Vol 8 (5) ◽

pp. 2359-2369 ◽

Cited By ~ 2

Author(s):

Bibhu Prasad Ganthia ◽

Keyword(s):

Genetic Algorithm ◽

Induction Motor ◽

Direct Torque Control ◽

Torque Control ◽

Motor Drive ◽

Induction Motor Drive ◽

Matlab Simulation

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A New Strategy-Based PID Controller Optimized by Genetic Algorithm for DTC of the Doubly Fed Induction Motor

Systems ◽

10.3390/systems9020037 ◽

2021 ◽

Vol 9 (2) ◽

pp. 37

Author(s):

Said Mahfoud ◽

Aziz Derouich ◽

Najib EL Ouanjli ◽

Mohammed EL Mahfoud ◽

Mohammed Taoussi

Keyword(s):

Genetic Algorithm ◽

Nonlinear Systems ◽

Induction Motor ◽

Pid Controller ◽

Hybrid Approach ◽

Direct Torque Control ◽

Harmonic Distortion ◽

Torque Control ◽

Doubly Fed ◽

Rejection Time

Proportional Integral Derivative (PID) is the most popular controller used in automatic systems, because of its robustness, ability to adapt the behaviors of the system, making them converge toward its optimum. These advantages are valid only in the case of the linear systems, as they present poor robustness in nonlinear systems. For that reason, many solutions are adopted to improve the PID robustness of the nonlinear systems. The optimization algorithm presents an efficient solution to generate the optimums PID gains adapting to the system’s nonlinearity. The regulation speed in the Direct Torque Control (DTC) is carried out by the PID controller, which caused many inconveniences in terms of speed (overshoot and rejection time), fluxes, and torque ripples. For that, this work describes a new approach for DTC of the Doubly Fed Induction Motor (DFIM) powered by two voltage inverters, using a PID controller for the regulation speed, based on a Genetic Algorithm (GA), which has been proposed for adjustment and optimizing the parameters of the PID controller, using a weighted combination of objective functions. To overcome the disadvantages cited at the beginning, the new hybrid approach GA-DTC has the efficiency to adapt to the system’s nonlinearity. This proposed strategy has been validated and implemented on Matlab/Simulink, which is attributed to many improvements in DFIM performances, such as limiting speed overshoot, reducing response time and the rate of Total Harmonic Distortion (THD) of the stator and rotor currents, and minimizing the rejection time of speed and amplitude of the torque and flux ripples.

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