In
the past few years, there has been an increasing interest in the application of
the fuzzy set theory to many control problems. For many complex control systems,
the construction of an ordinary model is difficult due to nonlinear and time varying
nature of the system. Fuzzy Control has been applied in traditional control systems,
which yields promising results, It is applied for the processes, which yields
promising results, it is applied for the processes, which are too complex to be
analyzed by conventional techniques or where the available information is uncertain.
In fact, fuzzy logic controller (FLC) is easier to prototype, simple to describe
and verify, can be maintained and also extended with grater accuracy in less time.
These advantages make fuzzy logic technology to be used for irrigation system
also.
NEED
FOR MODERN IRRIGATION SYSTEM Water and electricity should be optimally
utilized in an agricultural like India. The development in the filed of science
and technology should be appropriately used in the field of agriculture for better
yields. Irrigation has traditionally resulted in excessive labour and nonuniformity
in water application across the filed. Hence, an automatic irrigation system is
required to reduce the labour cost and to give uniformity in water application
across the field.
PHYSIOLOGICAL
PROCESSING In the irrigation system, plant take-varying quantities of
water at different stages of plant growth. Unless adequate and timely supply of
water is assured, the physiological activities taking place within the plant are
bound to be adversely affected, thereby resulting in reduced yield of crop. The
amount of water to be irrigated in an irrigation schedule depends upon the evapotranspiration(ET)
from adjacent soil and from plant leaves at that specified time. The rate of ET
of a given crop is influenced by its growth stages, environmental conditions and
crop management. The consumptive use or evapotranspiration for a given crop at
a given place may vary through out the day, through out the month and through
out the crop period. Values of daily consumptive use or monthly consumptive use
are determined for a given crop and at a given place. It also varies from crop
to crop. There are several elimatological factors, which will influence and decide
the rate of evaporation. Some of the important factors of elimate influencing
the evaporation are radiation, temperature, humidity and wind speed. In this work,
the input variables chosen for the system are evapotranspiration and rate of change
of evapotranspiration called as error and the output variable is water amount.
FUZZIFICATION
UNIT It converts a crisp process state into a fuzzy state so that it is
compatible with the fuzzy set representation of the process required by the inference
unit.
KNOWLEDGE BASE The Knowledge
base consists of two components. A rule base, which describes the behaviour of
control surfaces, which involves writing the rules that tie the input values to
the output model properties. Rule formation can be framed by discussing with the
experts. A database contains the definition of the fuzzy sets representing the
linguistic terms used in the rules. The knowledge base is generally represented
by a fuzzy associative memory.
INFERENCE
UNIT This unit is the core of the fuzzy controller. It generates fuzzy
control actions applying the rules in the knowledge base to the current process
state. It determines the degree to which each measured valued is a member of a
given labeled group. A given measurement can be classified simultaneously as belonging
to several linguistic groups. The degree of fulfillment (DOF) of each rule is
determined by applying the rules of Boolean algebra to each linguistic group that
is part of the rule. This is done for all the rules in the system. Finally the
net control action is determined by weighting action associated with each rule
by degree of fulfillment.
