Fuzzy logic to control dam system for irrigation and flooding
Ms. Neelam P. Naik
Late Bhausaheb Hiray S.S. Trust’s Institute of Computer Application,
Government Colony, Bandra (East), Mumbai 400 051.
Abstract:
level, amount of water discharge per unit of time,
Irrigation is used to increase agricultural production to
maximum possible point of outflow etc.
satisfy increasing need of increasing population. Dam
In this paper new method is proposed to control
control system takes information about water level, gate
reservoir of water. This method is based on fuzzy logic
opening ratios, gate operation as parameters and controls
control (FLC). Algorithm of simulated evolution (SEA)
spillway in case of flooding. In this paper a new method is
is used to obtain optimized membership function
proposed to control reservoir. The method is based on fuzzy
logic control and iterative algorithms for solving hard
representing fuzzy values.
combinatorial optimization problems such as algorithm for
FLC and SEA are introduced in section II. The control
simulated evolution. The proposed method is more reliable
problem is explained in section III. The section IV, the
and efficient over existing systems.
proposed FLC based on SEA is explained. The
conclusion is given in section V.
Keywords: Fuzzy logic control, Evolutionary
II. FUZZY LOGIC CONTROL AND SIMULATED
algorithm for optimization, dam control
EVOLUTION ALGORITHM
I. INTRODUCTION
FUZZY LOGIC CONTROL
The control system to reservoir management in a dam,
Fuzzy Logic Control system is based on fuzzy set
controls spillway gate and manages flow of discharged
theory [1]. This set theory is advanced version of
water. The range for the water level is prescribed
classical set theory called crisp theory. In crisp set
initially. The control system keeps the reservoir water
theory, an element either belongs to or does not belong
level in prescribed range. This operation is carried out
to a set. But fussy set supports a flexible sense of
for maximum utilization of water in dam. The
membership of elements to a set. Many degrees of
nonlinearities occur in reservoir water level flow and
membership, between 0 and 1, are allowed. The
these nonlinearities are unexpected. Hence the design of
membership function is associated with a fuzzy set in
reservoir operating system is challenging work. The
such a way that the function maps every element of the
aim of this fuzzy logic based control system is to adjust
universe of discourse or the reference set to the interval
the dam lake level to the desired set points in the
[0, 1]. In crisp logic, the truth values acquired by
shortest time possible by adjusting the openness of
propositions or predicates are two-valued, namely
spillway gates. Various uncertain factors that affect
TRUE or FALSE which may be treated numerically
dam water reservoir and flow are inflow hydrograph,
equivalent to (0, 1). However, in fuzzy logic, truth
unexpected and sudden changes in reservoir water
values are multivalued such as absolutely true, partly
true,
1
absolutely
false,
very true, and so on and are numerically equivalent to
system runs. Defuzzification is the name for a
any value in the range 0 to 1.
procedure to produce a real (non-fuzzy) output which
combines the fuzzy rule results together. It generally
takes one of several forms; in this case, each output is
Fuzzy Logic
Controller
Knowledge base
the weighted sum of applicable rules. The effect is to
interpolate outputs between the points specified by the
Rule base
Data base
rules.
Fuzzification Unit
Each of these sets has a triangular membership
Defuzzification Unit
Inference Unit
function, with a constant width. The outputs produced
are the positional corrections to apply to each of the
joints. Each joint has its own independent set of rules;
i.e., the state of other joints is not considered when
Dam water
reservior system
Inputs
calculating corrections to a specific joint.
Sensors
SIMULATED EVOLUTIONARY ALGORITHM
Output
In this work the applied fuzzy system contains fuzzy
Figure 1: Basic structure of Fuzzy Logic
rules. The premises and conclusions of fuzzy rules are
Controller[1]
described by fuzzy variables. Algorithm of simulated
Fuzzy logic allows inclusion of expert knowledge in
evolutionary algorithm (SEA) is used to obtain
control system [6]. A fuzzy logic system contains sets
optimized membership function representing fuzzy
used to categories input data (fuzzification), decision
values. During evolution, fuzzy controllers which
rules that are applied to each set, and a way of
generating
an
output
from
the
rule
contain fuzzy sets and rules, are configured again by
results
genetic operators in order to maximize fitness of the
(defuzzification). In the fuzzification stage, a data point
control
is assigned a degree of membership (DOM) in the each
[6].
Evolutionary
algorithm
carries
out
optimization of fuzzy rule based system by tuning and
set. The DOM is determined by a membership function.
learning fuzzy rules. Here tuning is related to the
The width of the membership function w is then set so
optimization of membership function. And learning
that adjacent sets overlap, ensuring that the total degree
constitutes automated design for fuzzy rule set, starting
of membership is constant. The rules are if...then
from scratch [6]. According to the Pittsburgh approach,
statements, e.g. IF input1=tiny AND input2=small
entire rule set is considered as a chromosome [6]. The
THEN output1=3. Each rule inherits a degree of
population consists of various fuzzy rule sets in which
membership (or a degree of applicability) which is the
are subject to selection mechanism, crossover and
product of the degrees of membership of the inputs.
mutation operators. The advantage of EA is that it
These rules may be input by a human expert before the
reduces design time and design cost.
2
GENETIC ENCODING
3.
Add new fuzzy rule
For the fuzzy system special genetic operators are
4.
Remove random, existing fuzzy rule.
provided to allow it for evolution. For example, ns and
5.
Add new input or output
nr fields define number of fuzzy sets and number of
6.
Remove random, existing input or output
fuzzy rules respectively. Field fs defines fuzzy sets and
field fr defines fuzzy rules. A single fuzzy rule is
III. PROBLEM DESCRIPTION
defined by nonzero integer values. This integer value
represents the number of inputs in the premise part, the
The reservoir of water is used for water supply and it
fuzzy set used for the fuzzification of the input, the
also directly affects flooding. The basic diagram
number of outputs in the conclusion part and the fuzzy
showing functioning of dam is shown in figure 2[1].
set used for the defuzzification of the output. Each
fuzzy rule may have a different number of conclusions
Gate
Weather Interval
and premises.
l
Outflow water (o)
h
CROSSING OVER
Inflow water (i)
hm
Dam
The crossing over used here is the combination of two
methods.
1.
Based on the fixed number of crossover points,
Figure 2: Structure of dam [1]
operators cut and exchange genetic
In above diagram various symbols used and their
information.
2.
meanings are as follows.
Specific information taken from parents is
i = inflow water
averaged.
o = outflow water
Fuzzy sets and fuzzy rules are the information inherited
h = dam lake level
from parent fuzzy system. During crossover operation
hm = minimum dam lake level
all the fussy sets from both parents are copied. And
l = openness of spillway gate
same one are used only once. The number of fuzzy
rules derived from parents is randomly selected. In the
In any condition, reservoir control system adjusts water
new fuzzy rule, number of inputs and outputs are
flow through spillway gate and keeps water level in
randomly selected from parents.
predetermined ranges. Due to heavy rain or overflow
due to any other reason, it is always necessary to
MUTATION
control reservoir effectively within less time. If control
Various mutation types used during optimization are as
system is handled by human being then there are
follows [6].
1.
Add new fuzzy set
2.
Remove random, existing fuzzy set.
always chances of incorrect decision and it may lead to
uncontrollable situation. The current system overcomes
this situation.
3
3.
IV. THE PROPOSED FUZZY CONTROL
If dam lake level is high and its time rate of
change is small positive then the openness of
SYSTEM
spillway gate is at middle.
The proposed fuzzy control system is shown in Fig.
4.
3[1].
If dam lake level is very high and its time rate
of change is small negative then the openness
of spillway gate is low.
Calculation of
lake water level
5.
h
dh
If dam lake level is very very high and its time
rate of change is big negative then the
openness of spillway gate is high.
Fuzzy Logic
Controller
Sensor
Initially membership functions are defined randomly.
l
Water
inflow (i)
Dam
EA algorithm is used to select the most appropriate
parameter values characterizing the fuzzy membership
Water level
in lake (h)
function. During optimization process EA algorithm
tries to minimize peak value of outflow and changes in
Figure 3: The proposed fuzzy controlled system
peak values.
Dam lake level (h) and dh is its time rate of change.
V. CONCLUSION
These are the input variables of the FLC. The openness
(l) of the spillway gate is the output variable and it is
In this paper, reservoir of water in dam is controlled by
controlled by the fuzzy controller. Here for dam lake
efficiently and accurately. For the optimization of the
level (h) desired set points are considered as 118m and
membership function simulation evolution algorithm is
127m. (say) [1]. The aim of the controller is to adjust
used. The fuzzy logic based control, optimized by
dam lake level in set points only within shortest time by
simulation algorithm provides effective and accurate
adjusting spillway gate openness. The boundary points
alternative for human operator. Also compared to other
for dh will be -1 and 1. The set points for l are
global optimization techniques, evolutionary algorithms
considered as 0 and 12.
(EA) are easy to implement and very often they provide
The following rule base is initially constructed
adequate solutions
randomly.
1.
If dam lake level is low and its time rate of
VI. REFERENCES
change is small positive then the openness of
spillway gate is very very low.
2.
[1] Aytekin Bagis, Dervis Karaboga and Tefaruk
If dam lake level is at middle and its time rate
Haktanir, “A new method for reservoir control of
of change is zero then the openness of spillway
dams”, Hydrological Processes, Vol. 18/13, pages
gate is very low.
2485- 2501, 2004
4
[2]
Fredrik
Nielsen,
“Simulated
[5] C. Grosan and A. Abraham, “Hybrid Evolutionary
evolutionary
optimization of an ion-exchange chromatography
Algorithms:
step”., Lund University, Department of Chemical
Reviews”, Hybrid Evolutionary Algorithms, pp. 1-17,
Engineering, Master thesis for Marcus Degerman since
2007.
2004
[6]
[3] David B. Fogel, “An Introduction to Simulated
“Evolutionary
Evolutionary Optimization”, IEEE Transactions on
Controllers”, Foundations of Computing and Decision
Neural Networks, Vol. 5, No. 1, January 1994.
Sciences, Vol. 33, pages 351-367, 2008
[4] Jeff Riley and Vic Ciesielski , “Evolution of Fuzzy
[7] Piero P. Bonissone, Raj Subbu, Kareem S. Aggour ,
rule based controllers for dynamic environments”.,
“Evolutionary Optimization of Fuzzy Decision Systems
Recent Advances in Simulated Evolution and Learning,
for Automated Insurance Underwriting” , 0-7803-7280-
Advances in Natural computation, Vol. II, (Ed) K. C.
8 , page 1003-1008, IEEE 2002
Tan, Chapter 23, page 426 - 445, World Scientific
2004.
5
Maciej
Methodologies,
HAPKE,
Design
Architectures,
Maciej
of
and
KOMOSINSKI,
Interpretable
Fuzzy