Tuning Of PID Speed Controller in FOC of Induction Motor Based On Particle Swarm Optimization, Genetic Algorithm and Fuzzy Logic

2008 ◽  
Vol 2 (2) ◽  
pp. 31-37
Author(s):  
P. Lakshmana Kumar ◽  
P. Dhana Selvi
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Speed Controller

scholarly journals Sensorless Rotor-Field Oriented Controlled Induction Motor Drive with Particle Swarm Optimization Algorithm Speed Controller Design Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yung-Chang Luo ◽  
Zhi-Sheng Ke ◽  
Ying-Piao Kuo
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Controller Design ◽  
Particle Swarm ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Swarm Optimization ◽  
Speed Controller

A sensorless rotor-field oriented control induction motor drive with particle swarm optimization algorithm speed controller design strategy is presented. First, the rotor-field oriented control scheme of induction motor is established. Then, the current-and-voltage serial-model rotor-flux estimator is developed to identify synchronous speed for coordinate transformation. Third, the rotor-shaft speed on-line estimation is established applying the model reference adaptive system method based on estimated rotor-flux. Fourth, the speed controller of sensorless induction motor drive is designed using particle swarm optimization algorithm. Simulation and experimental results confirm the effectiveness of the proposed approach.

scholarly journals Direct Torque Control of Induction Motor by Using Particle Swarm Optimization Technique

2019 ◽  
pp. 560-565
Author(s):  
Mohit Kumar Yadav ◽  
Somnath Sharma ◽  
Sumati Srivastava
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Optimization Technique ◽  
Direct Torque Control ◽  
Particle Swarm ◽  
Torque Control ◽  
Swarm Optimization ◽  
Speed Controller ◽  
Particle Swarm Optimization Technique ◽  
Improved Performance

This paper is based on an efficient and reliable evolutionary approach of particle swarm optimization (PSO) using direct torque control (DTC) of induction motor. In order to resolve the problem of parameter variation the PI controllers are generally used in industrial plants because it is uncomplicated and robust. However, there is a problem in changing PI parameters. So, the engineers are looking for automatic tuning procedures. In traditional direct torque-controlled induction motor drive, there is generally undesired torque and ripple in form of flux. So Tuning PI parameters (Kp, Ki) are critical to DTC system to improve the performance of the system. In this paper, particle swarm optimization (PSO) is planned to correct the parameters (Kp, Ki) of the speed controller in order to get improved performance of the system and also responsible to run the machine at base speed.

scholarly journals Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Hamid Reza Mohammadi ◽  
Ali Akhavan
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Steady State ◽  
Equivalent Circuit ◽  
Induction Motor ◽  
Particle Swarm ◽  
Circuit Parameter ◽  
Swarm Optimization ◽  
Full Load

A cost effective off-line method for equivalent circuit parameter estimation of an induction motor using hybrid of genetic algorithm and particle swarm optimization (HGAPSO) is proposed. The HGAPSO inherits the advantages of both genetic algorithm (GA) and particle swarm optimization (PSO). The parameter estimation methodology describes a method for estimating the steady-state equivalent circuit parameters from the motor performance characteristics, which is normally available from the nameplate data or experimental tests. In this paper, the problem formulation uses the starting torque, the full load torque, the maximum torque, and the full load power factor which are normally available from the manufacturer data. The proposed method is used to estimate the stator and rotor resistances, the stator and rotor leakage reactances, and the magnetizing reactance in the steady-state equivalent circuit. The optimization problem is formulated to minimize an objective function containing the error between the estimated and the manufacturer data. The validity of the proposed method is demonstrated for a preset model of induction motor in MATLAB/Simulink. Also, the performance evaluation of the proposed method is carried out by comparison between the results of the HGAPSO, GA, and PSO.

A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

2016 ◽  
Vol 6 ◽  
pp. 25
Author(s):  
LingZhi Yi ◽  
Sui YongBo ◽  
Yu WenXin
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Speed Controller ◽  
Inertia Coefficient ◽  
Improved Performance ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia

Optimization techniques are becoming more popular for the improvement in control of induction motor. Many intelligent algorithms have been used to improve performance of induction motor so for including particle swarm optimization. However, the improved performance may be limited on account of inertia coefficient in particle swarm optimization, which lead to the unbalance between the searching step and searching precision. In this paper, a variable-step nonlinear dynamic inertia weight of particle swarm optimization speed controller is proposed to improve the performance of an induction motor. The experiment results show that the proposed method has excellent performance.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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