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

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

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

A new control system design for a small hydro-power plant based on particle swarm optimization-fuzzy sliding mode controller with Kalman estimator

2011 ◽  
Vol 34 (4) ◽  
pp. 388-400 ◽  
Author(s):  
A Zargari ◽  
R Hooshmand ◽  
M Ataei
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Sliding Mode ◽  
Particle Swarm ◽  
Local Network ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Hydro Power ◽  
Fuzzy Sliding Mode ◽  
System Variables

One of the main problems in small hydro-power plants that are locally used is their frequency control system. In this paper, a suggested control system based on the fuzzy sliding mode controller is presented for controlling the network frequency. Also, the proposed control strategy is compared with a PI controller and conventional sliding mode controller. In order to regulate the membership functions of fuzzy system more accurately, the particle swarm optimization algorithm is also applied. Moreover, because of unavailability of the control system variables, an estimator is suggested for estimating and identifying the system variables. This estimator will reduce the costs of implementing the control method. The simulation results show the ability of controller system in controlling the local network frequency in the presence of load and parameter’s variations.

Application of an Improved Internal Model Controller Based on the Particle Swarm Optimization in Network Control System

2013 ◽  
Vol 397-400 ◽  
pp. 1137-1144
Author(s):  
Wei Chen ◽  
Wen Bin Wang ◽  
Zhi Kai Zhao ◽  
Zhi Yuan Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Internal Model ◽  
Particle Swarm ◽  
Internal Model Control ◽  
Network Control ◽  
Step Response ◽  
Network Control System ◽  
Swarm Optimization ◽  
Internal Model Controller

Internal Model Control (IMC) is widely used in Network Control System (NCS) with its strong robustness and simple parameter adjustment. But the accurate dynamic inversion of the IMC model is not easy to find out. To solve this problem, an improved Internal Model Controller is designed with a PID controller and feedback loop, then the Particle Swarm Optimization (PSO) is used to optimize all the parameters of the improved controller. At last, simulation results show that the improved Internal Model Controller can maintain the system stability and the performance of the step response is extremely great in terms of rapidity and anti-interference ability, compared with the classic internal model controller, which enables NCS to achieve a better control effect.

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.

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