scholarly journals The Design of Frequency Filters of Iterative Feedback Tuning Using Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Arman Sharifi
Keyword(s):  
Particle Swarm Optimization ◽  
Closed Loop ◽  
Particle Swarm ◽  
Weighting Coefficient ◽  
Swarm Optimization ◽  
New Approach ◽  
Quadratic Performance Index ◽  
Quadratic Performance ◽  
Iterative Feedback

Iterative feedback tuning (IFT) is a data-based tuning approach that minimizes a quadratic performance index using some closed-loop experimental data. A control weighting coefficient, known as lambda, and two frequency filters are the most important parameters which can significantly improve the performance of the method. One of the major problems in IFT is tuning these parameters. This paper presents a new approach to tune frequency filters using particle swarm optimization (PSO). At the end, the performance of the proposed method is evaluated by two case study simulations.

Research and Analysis of Discrete PID Controller Parameters’ Optimization Based on Particle Swarm Optimization Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1228-1232 ◽  
Author(s):  
Zhan Jun Huang ◽  
An Na Wang ◽  
Cui Lei
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Performance Index ◽  
Pid Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Good Effect ◽  
Swarm Optimization ◽  
Quadratic Performance Index ◽  
Quadratic Performance

Currently, PID controllers have been widely used in industrial control. And computer control systems are widely used in the field environment. How to design a discrete PID controller which has a good control performance is very important for existing control objects. In this paper, particle swarm optimization and quadratic performance index are used to optimize the control parameters of the discrete PID controller. The optimized results are given at last. By simulation studies and analysis, the optimal parameters of the PID controller are found by particle swarm optimization algorithm, and the quadratic performance index has a good effect on optimal control.

A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY

2010 ◽  
Vol 09 (02) ◽  
pp. 87-104 ◽  
Author(s):  
JIANSHENG WU ◽  
MINGZHE LIU ◽  
LONG JIN
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Support Vector Regression ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Support Vector Regression Model ◽  
Swarm Optimization

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application.

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