Research on Double Closed-Loop DC Speed-Regulation System Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 846-847 ◽  
pp. 317-320 ◽  
Author(s):  
Le Peng Song ◽  
Han Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Step Response ◽  
Regulation System ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Improved Pso

For the defects of the parameter tuning and optimization of the PID controller uses an improved Particle Swarm Optimization (IPSO) algorithm to apply on the dual closedloop DC speed tuning system and adjust PID controller parameters online. The optimization result of adopting step response of the improved PSO algorithm is analyzed. It shows that using the improved PSO algorithm will obtain better dynamic performance, follow faster and more robustness than the traditional engineering design method. It provides a good performance of practical method for PID parameters optimization.

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 394 ◽  
pp. 505-508 ◽  
Author(s):  
Guan Yu Zhang ◽  
Xiao Ming Wang ◽  
Rui Guo ◽  
Guo Qiang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Genetic Mutation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Optimum Path ◽  
Improved Pso

This paper presents an improved particle swarm optimization (PSO) algorithm based on genetic algorithm (GA) and Tabu algorithm. The improved PSO algorithm adds the characteristics of genetic, mutation, and tabu search into the standard PSO to help it overcome the weaknesses of falling into the local optimum and avoids the repeat of the optimum path. By contrasting the improved and standard PSO algorithms through testing classic functions, the improved PSO is found to have better global search characteristics.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Application of Particle Swarm Optimization in Design of PID Controller for AVR System

2013 ◽  
Vol 2 (3) ◽  
pp. 1-17 ◽  
Author(s):  
H. F. Abu-Seada ◽  
W. M. Mansor ◽  
F. M. Bendary ◽  
A. A. Emery ◽  
M. A. Moustafa Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Stability Margin ◽  
Pso Algorithm ◽  
Step Response ◽  
Voltage Response ◽  
Swarm Optimization ◽  
Avr System ◽  
Terminal Voltage

This paper presents a method to get the optimal tuning of Proportional Integral Derivative (PID) controller parameters for an AVR system of a synchronous generator using Particle Swarm Optimization (PSO) algorithm. The AVR is not initially robust to variations of the power system parameters. Therefore, it was necessary to use PID controller to increase the stability margin and to improve performance of the system. Fast tuning of optimum (PID) controller parameter yield high quality solution. New criteria for time domain performance evaluation was defined. Simulation for comparison between the proposed method and Ziegler-Nichols method is done. The proposed method was indeed more efficient also. The terminal voltage step response for AVR model will be discussed in different cases and the effect of adding rate feed back stabilizer to the model on the terminal voltage response. Then the rate feedback will be compared with the proposed PID controller based on use of (PSO) method to find its coefficients. Different simulation results are presented and discussed.

An Improved Particle Swarm Optimization Algorithm

2012 ◽  
Vol 538-541 ◽  
pp. 2658-2661
Author(s):  
Ri Su Na ◽  
Qiang Li ◽  
Li Ji Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Negative Gradient ◽  
Improved Pso

Based on the standard particle swarm optimization an improved PSO algorithm was introduced in this paper. The particle swarm optimization algorithm with prior low precision, divergent character and slow late convergence is improved by joining the negative gradient. By adding negative term on standard PSO formula, combining with coefficient of negative gradient term and inertia weight , lead to effectively balance between the local and global search ability. It will accelerate convergence and avoid local optimum. Moreover, from the bionic point of view, this improved PSO algorithm is closer to the reality of the actual situation of the bird flocking. From the simulation results of four typical test functions, it can be seen that an improved particle swarm optimization with negative gradient can significantly improve the solving speed and solution quality.

scholarly journals Anti-synchronization of discrete-time chaotic systems using optimization algorithms

2011 ◽  
pp. 143-147
Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Pid Controller ◽  
Chaotic Systems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Modified Particle Swarm Optimization

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.

Design and Simulation of an Improved Particle Swarm Optimization

2010 ◽  
Vol 20-23 ◽  
pp. 1280-1285
Author(s):  
Jian Xiang Wei ◽  
Yue Hong Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Dimensional Space ◽  
Numerical Test ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Diversity ◽  
Test Problems ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Pso

The particle swarm optimization (PSO) algorithm is a new population search strategy, which has exhibited good performance through well-known numerical test problems. However, it is easy to trap into local optimum because the population diversity becomes worse during the evolution. In order to overcome the shortcoming of the PSO, this paper proposes an improved PSO based on the symmetry distribution of the particle space position. From the research of particle movement in high dimensional space, we can see: the more symmetric of the particle distribution, the bigger probability can the algorithm be during converging to the global optimization solution. A novel population diversity function is put forward and an adjustment algorithm is put into the basic PSO. The steps of the proposed algorithm are given in detail. With two typical benchmark functions, the experimental results show the improved PSO has better convergence precision than the basic PSO.

Parameter Optimization of PID Controller Based on an Improved Particle Swarm Optimization for the Induction Motor

2011 ◽  
Vol 130-134 ◽  
pp. 1938-1942
Author(s):  
Xia Bo Shi ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Parameter Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Motor Speed ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Ipso Algorithm

This paper presents a new approach of PID parameter optimization for the induction motor speed system by using an improved particle swarm optimization (IPSO). The induction motor speed is changed by the stator voltage controlled with PID controller. The performance of PID controller based on IPSO is compared to Linearly Decreasing Inertia Weight (LIWPSO). Simulation results demonstrate that the IPSO algorithm has better dynamic performance, higher accuracy and faster convergence and good performance for the PID controller.

Double-Loop PID Parameter Tuning for DC Motor Based on Modified Particle Swarm Optimization

2011 ◽  
Vol 130-134 ◽  
pp. 3139-3142
Author(s):  
Tao Cheng ◽  
Wei Xing Lin
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
High Performance ◽  
Dynamic Performance ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Parameters Optimization ◽  
Double Loop ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

This paper proposes a modified particle swarm optimization to solve identification of tuning PID controller parameters. This paper elaborates the process that MPSO algorithm optimizes PID parameters in double-loop speed control system modeled by simulink. Through analyzing the results of the MPSO optimization, and comparing with standard PSO(SPSO) and traditional method, MPSO algorithm has better dynamic performance, provides a high performance methods for PID parameters optimization.

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