19-07-2012, 02:23 PM
Design of PID Controller for Higher Order Continuous Systems using MPSO based Model Formulation Technique
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INTRODUCTION
PID controllers have been widely used in industries for
various applications and it plays a vital role in automation.
It has been a crucial problem to tune properly the gains of the
PID controller because many industrial plants are often
burdened with the characteristics such as higher order, time
delay and nonlinearities [1]. While modeling the complex
systems like aircraft mechanism, Atomic plant process
monitoring, fuel injector and spark timing of auto mobiles it
can be noted that the system order is increased. The analysis
and synthesis of higher order systems are difficult and
generally not desirable on economic and computational
considerations. Thus, it is necessary to obtain a lower order
system so that the obtained lower order maintains the
characteristics of the original system. This helps in minimizing
the variations during design and realization of suitable control
system components to be attached to the original system.
OVERVIEW OF PARTICLE SWARM OPTIMIZATION
The particle swarm optimization (PSO) technique appeared
as a promising algorithm for handling the optimization
problems. PSO is a population-based stochastic optimization
technique, inspired by social behavior of bird flocking or fish
schooling [19]. PSO is inspired by the ability of flocks of
birds, schools of fish, and herds of animals to adapt to their
environment, find rich sources of food, and avoid predators by
implementing an information sharing approach. PSO
technique was invented in the mid 1990s while attempting to
simulate the choreographed, graceful motion of swarms of
birds as part of a socio cognitive study investigating the notion
of collective intelligence in biological populations.
MODIFIED PARTICLE SWARM OPTIMIZATION
In this new proposed modified PSO having better
optimization result compare to general PSO by splitting the
cognitive component of the general PSO into two different
component. The first component can be called good
experience component. This means the bird has a memory
about its previously visited best position. This is similar to the
general PSO method. The second component is given the
name by bad experience component. The bad experience
component helps the particle to remember its previously
visited worst position. To calculate the new velocity, the bad
experience of the particle also taken into consideration [20].
CONCLUSION
The quality of a formulated lower order model is judged by
designing the PID controller. PID controller of the formulated
lower order system effectively controls the original high order
system. The main advantage of the proposed method is that it
is easy of implementation and least elapsed time. The
proposed approach can also be used for designing a discrete
PID controller. This can also extended for other evolutionary
techniques and hybrid methods and also its extended for
further design of compensators as well as state variable
controllers and observers for stabilization process.