Particle Swarm Optimization with Convergence Speed Controller for Sampling-Based Image Matting

2018 ◽  
pp. 656-668 ◽  
Author(s):  
Yihui Liang ◽  
Han Huang ◽  
Zhaoquan Cai ◽  
Liang Lv
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Image Matting ◽  
Swarm Optimization ◽  
Speed Controller

Optimal gain tuning of PI speed controller in induction motor drives using particle swarm optimization

2010 ◽  
Vol 19 (2) ◽  
pp. 343-356 ◽  
Author(s):  
R. Thangaraj ◽  
T. R. Chelliah ◽  
M. Pant ◽  
A. Abraham ◽  
C. Grosan
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Particle Swarm ◽  
Motor Drives ◽  
Induction Motor Drives ◽  
Swarm Optimization ◽  
Speed Controller

Automatically Terminated Particle Swarm Optimization with Principal Component Analysis

2015 ◽  
Vol 14 (01) ◽  
pp. 171-194 ◽  
Author(s):  
Bun Theang Ong ◽  
Masao Fukushima
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Convergence Speed ◽  
Swarm Optimization ◽  
Termination Criteria ◽  
Hybrid Particle Swarm Optimization ◽  
Speed Accuracy

A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called "Gene Matrix" to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant.

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 Off-Line Tuning of Positioning Control System using Particle Swarm Optimization Considering Speed Controller

2007 ◽  
Vol 127 (1) ◽  
pp. 52-59 ◽  
Author(s):  
Takayuki Kaneko ◽  
Hiroyuki Matsumoto ◽  
Hironori Mine ◽  
Hideyuki Nishida ◽  
Tomoharu Nakayama
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Positioning Control ◽  
Speed Controller

Offline tuning of positioning control system using particle swarm optimization considering speed controller

2008 ◽  
Vol 163 (3) ◽  
pp. 68-77 ◽  
Author(s):  
Takayuki Kaneko ◽  
Hiroyuki Matsumoto ◽  
Hironori Mine ◽  
Hideyuki Nishida ◽  
Tomoharu Nakayama
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Positioning Control ◽  
Speed Controller

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.

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