16-08-2012, 02:52 PM
Auto Tuning PID Controller based on Improved Genetic Algorithm for Reverse Osmosis Plant
INTRODUCTION
According to a report from UNESCO, by the middle of this
century, at worst 7 billion people in sixty countries will be
water scarce, at best 2 billion people in forty eight countries.
This expectation makes necessary for humanity to look for new
alternative ways of ensuring a dependable supply of drinking
water. The significance of this problem is increasing in the
underdeveloped countries as well as in industrialized regions.
Desalination of seawater and brackish water is one of the
alternatives for ensuring a dependable supply of drinking water.
In recent years the process of reverse osmosis (RO) has become
a significant technical option to solve this problem through the
desalination of seawater [1].
RO is a process used to clean brackish water or to desalt
seawater. The process consists in recovering water from a
saline solution pressurized by pumping it into a closed vessel to
a point grater than the osmotic pressure of the solution. Thus,
the solution is pressed against a membrane so that it is
separated from the solutes (the dissolved material).
PID CONTROLLER
The PID controller is well known and widely used to
improve the dynamic response as well as to reduce or eliminate
the steady state error. The derivative controller adds a finite
zero to the open loop plant transfer function and improves the
transient response. The integral controller adds a pole at the
origin, thus increasing system type by one and reducing the
steady state error due to a step function to zero.
PID control consists of three types of control, Proportional,
Integral, and Derivative control.
GENETIC ALGORITHM
The basic principles of GA were first proposed by Holland.
This technique was inspired by the mechanism of natural
selection, a biological process in which stronger individual is
likely to be the winners in a competing environment. GA uses a
direct analogy of such natural evolution to do global
optimization in order to solve highly complex problems [14]. It
presumes that the potential solution of a problem is an
individual and can be represented by a set of parameters. These
parameters are regarded as the genes of a chromosome and can
be structured by a string of concatenated values. The form of
variables representation is defined by the encoding scheme.
The variables can be represented by binary, real numbers, or
other forms, depending on the application data. Its range, the
search space, is usually defined by the problem.
EVALUATE THE PROCESS USING FITNESS FUNCTION
Objective function
The most crucial step in applying GA is to choose the
objective functions that are used to evaluate fitness of each
chromosome. Some works use performance indices as the
objective functions. The objective functions are Mean of the
Squared Error (MSE), Integral of Time multiplied by Absolute
Error (ITAE), Integral of Absolute Magnitude of the Error
(IAE), and Integral of the Squared Error (ISE)[8][11].
CONCLUSION
This paper demonstrated how an improved GA can be used
for the optimal control of RO plant via computer simulation. A
new GA method based on hybrid concept of Cauchy
distribution, linear congruential generator and simultaneous
using of RMSE type objective function to design a controller
for RO plant is presented. Much more improved performance
of proposed GA tuned controller than the conventional ones
has been revealed in terms of overshoot and settling time etc.
Real time implementation of the proposed method is under way.
Also at the same time implementation of micro controller based
on the new method in commercially low cost is being sought.
Although GA needs lot of computation, its real time realization
of the idea will be performed via physical experiment.