09-08-2012, 05:02 PM
Robust Control of DC Motor Using Fuzzy Sliding Mode Control And Particle SwarmOptimization “PSO” Algorithm
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INTRODUCTION
There are variety methods for DC motors control that are presented since now. The presented methods for
DC motors control are divided generally in three groups. Classic methods such as PID PI controllers (J.I.A.
Nuo, 2008; O. Tolga Altinoz, 2010).
Modern methods (adaptation-optimum,…) (Wu Wang, 2009; Viroch Sukontanakarn, 2009; Alexander
Bogdanov, 2004). Artificial methods such as neural networks and fuzzy (T. Sugie, 1998; Shin-ichi Horikawa,
1995). Theory are the presented methods for DC motors speed control.
The design method in linear control comprise based on main application the wide span ' of frequency, linear
controller has a weak application, because it can't compensate the nonlinear system effect completely.
Sliding Mode Controller:
Nonlinear system control that its model isn't clear carefully works with tow methods:
resistant control methods
comparative control methods
In control view, uncertainly in modeling is divided in two main kinds:
Non certainly in existent Para meters in model
Estimating the lower step for system and being UN modeled dynamics in e estimating model. Sliding
control is one of the designed modes for robust control that make access to system desired application
estimating system in model.
The major idea of this method is the controlling of nonlinear first grade system is easier than n grade system
control in spite of uncertainly.
Particle swarm Optimization (PSO) Algorithm:
Since the introduction of the particle swarm optimizer by James Kennedy and Russ Eberhart in 1995 (J.
Lam, 2004), numerous variations of the basic algorithm have been developed in the literature. Each researcher
seems to have a favorite implementation - different population sizes, different neighborhood sizes, and so forth.
In this paper we examine a variety of these choices with the goal of defining a canonical particle swarm
optimizer, that is, an off-the shelf algorithm to be used as a good starting point for applying PSO.The original
PSO formulae defined each particle as a potential solution to a problem in D-dimensional space,with particle i
represented Xi=(xi1,xi2,...,xiD). Each particle also maintains a memory of its previous best position,
Pi=(pi1,pi2,...,piD), and a velocity along each dimension, represented as Vi=(vi1,vi2,...,viD). At each iteration,
the P vector of the particle with the best fitness in the local neighborhood, designated g, and the P vector of the
current particle are combined to adjust the velocity along each dimension, and that velocity is then used to
compute a new position for the particle. The portion of the adjustment to the velocity influenced by the
individual’s previous best position (P) is considered the cognition component, and the portion influenced by the
best in the neighborhood is the social component.