Full parameterization process

Full Parameterization Process for Singleton Fuzzy
Logic Controllers: A Computing Algorithm
Osama A. Awad Rami A. Maher
College of Information Engineering. Dept. of Electrical Engineering
Nahrain University Isra University
Baghdad, Iraq Amman, Jordan
Abstract—Although fuzzy systems demonstrate their ability to
solve different kinds of problems in various applications, there is
an increasing interest on developing solid mathematical
implementations suitable for control applications such as that
used in fuzzy logic controllers (FLC). It is well known that, wide
range of parameters is needed to be specified before the
construction of a fuzzy system. To simplify in a systematic way
the design and construction of a general fuzzy system, and
without loss for generality a full parameterization process for a
singleton type FLC is proposed in this paper. The presented
methodology is very helpful in developing a universal computing
algorithm for a standard fuzzy like PID controllers. An
illustrative example shows the simplicity of applying the new
paradigm.
Keywords—parameterization;fuzzy logic controller(FLC);
singletone FLC ; PID
I. INTRODUCTION
Since Zadeh introduced the basics of fuzzy sets [1] in
1965, and the fuzzy logic concepts [2] in 1968; fuzzy logic
has been successfully applied to a wide range of applications
in various fields. Mamdani and Assilian [3] first applied the
fuzzy logic control in to the control field, and since then
fuzzy logic controllers have attracted a great deal of interest
among many researchers. Later on, fuzzy logic controller is
proven to be an effected way in control engineering
applications.
There are mainly two types of a ruled base fuzzy system.
One is the Mamdani type FLC [4], and the other is the
Takagi-Sugeno (TS) [5]. Structure for the both types are the
same, the only difference is related to the definition of the
output in the consequent field of the rule base. TS type uses a
crisp values for the output in the rule base, where it is a fuzzy
linguistic in the case of Mamdani type.
Another type gaining a wider acceptance in control and
industrial applications, which is called a singleton fuzzy
controller [6] will be adopted and focused on by this paper.
Although it defines a singleton membership function over the
output, it is actually uses a constant real value called a
singleton of the rule output, representing the position of the
trivial output MF. With this type several activation,
accumulation and defuzzification methods yield identical
results [7].
As the field of fuzzy computing is an active research field,
many methodologies are developed for constructing and
computing the FLC. The designer of a fuzzy controller for
certain control application is faced with the many design
choices that the fuzzy set theory provides. Fundamental
comparisons and suggestions are found in the literature, and
they are well presented by [8-11,16-18]. These computing
approaches are not unique; it is mainly due the lack of having
a general good mathematical formulation for the fuzzy
system construction algorithm.
A solution for this problem may be solved if good
parameterization process is developed. The parameterization
of a fuzzy system is insufficiently addressed in the literature.
This work is a trial to solve this problem, and mainly devoted
to present a fuzzy system for control applications, whether
used in construction of the FLC, or in fuzzy system modeling
or used in design and tuning of the FLC itself.
II. PROPLEM FORMULATION
Fuzzy logic system (of which FLC is a special application)
is a natural extension of fuzzy set theory to relations between
fuzzy sets and rules. A FLC is characterized by four
modules: [fuzzifier, inference engine, knowledge base, and
defuzzifier]. A schematic representation of FLC is presented
in Fig. 1.
Fig. 1. Basic structure of a FLC.
The parameters of an FLC can be classified into four
categories [12]: logical, structural, connective, and
operational as can be shown in Table I.
A. Parameterization Process of the FLC
In the following, we will discuss the suggested FLC
parameterization methodology specified for a singleton type
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fuzzy system, from different aspects according to the
classification of parameters summarized by Table I.
B. Parameterization of Input Membership Functions
Consider the fuzzy system as shown in Fig. 2. which is a
simplified form of Fig. 1 with input x and output u ,
where xn
x  is the fuzzy system input variables,
un
u  is the fuzzy system output variables, and { xn ,
un } are the dimension of input and output variables.
TABLE I. PARAMETER CLASSIFICATION OF A FLC
Fig. 2. A simplified input-output fuzzy block.
The input membership functions are parameterized by:


 ix nn
in (1)
where: ),......2,1;max( xxi nimcn i
 (2)
and c represents the standard number of parameters that
define a certain type of MF, ixm is the number of fuzzy sets
assigned for input ix . The i th row of in includes all the
parameters that characterize the MFs which are related to the
i th input (shape, no. of sets, width of the fuzzy set, spacing
and overlapping between the sets…).
Example 1:
For a fuzzy system with two inputs, each defined with three
triangular MFs, then xn 2 ; 31
xm ; 32
xm
and 3c .
A triangular MF is normally defined by three points (a, b, c).
Therefore in will be defined by
92
in







232323222222212121
131313121212111111
cbacbacba
cbacbacba
in (3)
As the triangular MF is characterized by its core (most fuzzy
systems employ a normalized fuzzy sets to specify the entire
partition of a fuzzy variables), a great number of reduction in
the number of parameters defining the input MF can be
introduced. Without loss of generality, this implementation is
adopted in the work. Hence (3) will be reduced to:







232221
131211
bbb
bbb
in (4)
C. Parameterization of Membership Degree
In FLC computing, only the degree of membership is
further processed. Often information is lost during this
procedure, although it is not required that the MFs are
normalized (i.e. their sum is equal to one for all x ), this
property is called fuzzy partition and often is employed
because it makes the interpretation and computation easier
[7]. The degree of membership is obtained for the current
input vector by:
ixm
xi
 (5)
and,
.......,3,2,1),,;( )(
xx
i
inix nimx ii
  (6)
where .)(.; is a generalized MF producing degrees of
membership for all fuzzy sets related to the input ix and
ix [
ixmk  ,......,.....,, 21 ] (7)
is a vector of dimension ixm , each element represents the
degree of membership for fuzzy subset )(k associated with
input ix and evaluated using the input parameter
)(i
in at a
given point in the range of the relative input.
Example 2:
Consider the previous example; membership degree  can
be evaluated for input 1x at a measured value 1cx by using
(7) as follows:
)3,;( 11
)1(
1  xinTx mcx 
 3211
 x (8)
where the index T is put as an indicator targeting for using
the triangular function in calculating the membership degree.
1]),[;( 11111111  kcbacxT
2]),[;( 12121212  kcbacxT (9)
3]),[;( 13131313  kcbacxT
The membership degree of each fuzzy set )3,2,1( k for a
triangular MF is evaluated by:
CLASS PARAMETERS
LOGICAL REASONING MECHANISM, FUZZY
OPERATORS, MEMBERSHIP FUNCTIONS
TYPE, DEFUZZIFICZTION METHOD
STRUCTURAL RELEVANT VARIABLES, NUMBER OF
MEMBERSHIP FUNCTIONS, NUMBER OF
RULES
CONNECTIVE ANTECEDENT PART OF THE RULE,
CONSEQUENT PART OF THE RULE, RULE
WEIGHTS
OPERATIONAL MEMBERSHIP FUNCTION VALUES
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)( 1cxk













0
0
11
11
11
11
kk
k
kk
k
bc
cxc
ab
acx
11
111
111
11
;
;
;
;
cxc
ccxb
bcxa
acx
k
kk
kk
k














(10)
at least one value of the vector 1x is not zero, otherwise the
value of the input 1x is not represented by any of the MFs
(fuzzy sets) defined over the relative UOD.
For an acceptable (50%) overlapping between MFs, it
is sufficient to parameterize the inputs by their cores
only as shown in Fig. 3.
D. Parameterization of Rule Base Premise
Without loss of generality, completeness of the rule
Fig. 3. Triangular input MF parameters represented by their cores.
base (RB) will be assumed, by taking all the possibilities
encountered by the predefined MFs over the input variables.
This approach, which is adopted by the presented
methodology, will cover both reduced and full RB
representation by letting the certainty of each undefined rule
to be zero, and set one otherwise.
Also, most of FLCs used in control application are
assumed to have fuzzy propositions connected with fuzzy
and connectives only. Thus the structure of the rules
premise is defined by:
xR nn
rules

 (11)
where Rn denotes the total number of rules in the RB, and
xn represents the number of inputs used in constructing the
FLC. Noting that Rn is defined by:


x
i
n
i
xR mn
1
(12)
a row of rules , connects the index of the input fuzzy sets for
each input variable defined by each rule, and hence reflects
the degree of membership ix function to be taken into
considerations for evaluating the premise certainty (truth
value) of the specified rule, defined some times in the
literature as degree of fulfillment )(dof or firing strength.
Accordingly, the firing strength of the rule is performed
using the generalized T-norm or T-conorm function given by:
R
j
rulesxxxx
j
njT nxi
,..2,1),;,....,....,,( )()(
21
  (1
3)
where (13) operates the T-norm or T-conorm between the
elements of the vectors defined by rules . This operation
represents the aggregation stage of the inference engine.
Example 3
Consider the same previous example, for which the premise
part of the complete RB will be constructed as follows:




1
1
rules
2
1
3
1
1
2
2
2
3
2
1
3
2
3
T



3
3
 





2
1
x
x
(14)
where the dimension of the RB according to (12) is 9Rn .
Then, to compute the dof for rule 6j , we will proceed
as follows:
1st
-Access (14) given j equal six, then get the index for the
MF defined for each input variable 2 k for
input 1x , and 3k for input 2x .
2nd-
Apply the T-norm on the degree of memberships
( 1x and 2x ) as given by (6), choosing product operator
(or minimum) to represent the T-norm, then (13) gives
the following:
 
);,( )6(6
21 rulesxxT  (15)
)()( 2312
)6(
xx   (16)
Hence, certainty of the 6th
rule is evaluated by the product of
the 2nd
MF value (defined by 2mf ) for input 1 (calculated at
the measurement value of input 1x ) and the 3rd
MF value
(defined by 3mf ) for input 2 (calculated at the measurement
value of input 2x ).
E. Parameterization of the Rule Base Output
After the dof has been calculated for all the rules, and, for
the inference engine process to be completed, it is required
now to consider the outputs of the RB.
Now, consider the output parameters vector by defining:
uR nn
out

 (17)
where un represents the number of controller outputs, Rn
represents the dimension of the rule base.
The i th row within out is defined by:
uiniji
i
out njhhh u
,....2,1];[ 1
)(
  . (18)
where ijh is a constant real value representing the place of
the singleton MF selected from a number of jum fuzzy sets
defined over the output ju at rule i .
Since the output membership value is always one at the core
and zero elsewhere, hence the outputs in the RB are always
defined by their singletons which are represented by:
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kij ch  ; k =1,2,3 …. jum (19)
Example 4
For a singleton FLC structure with one output ( 1un )
defined over five MFs ( 51
um ), then the RB consequence
of the previous example could be represented by the
following parameterization vector:
out [ 11h 21h 31h . . . . 81h 91h ]
T
(20)
and, for five singleton MFs defined by:
[ 1c 2c 3c 4c 5c ]
then out could be set as follows:
out [ 1c 2c 3c 2c 3c 4c 3c 4c 5c ]
T
(21)
for an arbitrary values of the output singleton MF cores, as
given by [ 1 7.0 0 7.0 1 ]
then, the output RB parameterization vector could be set by:
out [ 1 7.0 0 7.0 0 7.0 0 7.0 1]
T
(22)
F. Parameterization of the FLC Output
As a final stage for calculating the crisp output out of the
FLC, a defuzzificztion stage is mandatory for this purpose.
Many defuzzificztion formulas are developed [11], each is
suitable for a certain application. For control applications it
is found that using the center of area (COA), and a well
known version named the center of gravity (COG) are highly
recommended [8].
The output U of the FLC is evaluated by:



 Rn
i
i
T
out
U
1
)(

where  and out
respectively.
The generalized form of the defuzzified output can be written
in the following form:
),( outDU  (24)
where D can be any defuzzification formula applied to
evaluate the crisp output U .
G. Parameterization of Input and Output Variables
In closed loop systems as shown in Fig. 4., there are
several signals which should be taken into consideration
when the control signal is calculated. The error signal
between the set point

y and the measurement output
Fig. 4. Typical FLC in a closed loop control system.
variable y is observed, and given by:
)()()( *
kykyke  (25)
The main control objective is to keep the error signal as
small as possible. Also, the rate of change in the error signal
is given by:
)1()()(  kekeke (26)
with those two inputs { )(),( keke  } the FLC can perform
the PD or PI type control depending on whether the output is
taken to be the pure control signal )(ku or the change in the
control signal )(ku . Different PID like FLC structures can
be generated using the above concepts in various forms.
H. Parameterization of the Fuzzy Operators
There are multiple choices for representing the premise
fuzzy conjunction and , and fuzzy disjunction or operators. A
common choice is the (min-max) composition [13] and the
(product-sum) composition [14]. It is found that representing
the T-norm by any operator other than the product operator
will introduce un-adjustable nonlinearities [11]. In this work
product operator is used to implement both and conjunction
and the T-implication.
I. Parameterization of the scaling factors
Although scaling factors is not part of the parameters for a
fuzzy system, but practically, is highly acceptable to be part
of the FLC structure. Those scaling factors related to the
inputs Xg ( ee gg , ) and the outputs Ug ( uu gg , ) are
playing an important role in tuning the fuzzy controller and
for normalization the input and output UODs.
  /1],[ 11
 xx gUOD
  /1],[ 22
 xx gUOD
  /1],[ 11
 uu gUOD (27)
Actually they are part of the pre-processing and post-
processing stages.
III. FLC COMPUTING ALGORITHM
Back to Table 1, a complete set of FLC parameters are
defined and connected by systematic mathematical
formulation suitable for the computing algorithm, and
summarized as follows:
1) The inference mechanism uses the singleton fuzzy system
as given by the general RB structure:
IF ( rules ) THEN ( out ) (28)
2) Relevant inputs are { 1e , 1e } and outputs are
{ 1u , 1u }.
3) Number of MFs defined for each input and output is given
by ixm and jum .
4) Input MFs characteristics in are defined by (3), and
output MFs are defined by singletons (19).
5) The fuzzy operators are defined to use the product
(23)
are defined by (13) and (18)
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operator for calculating the rule premise certainty as
given by (13) and for calculating the implication as given
by numerator part of (23).
6) Number of rules is calculated by (12).
7) Antecedents of the rules are defined by rules (11).
8) Consequents of the rules are defined by out (18).
9) Membership values are calculated by (7).
10) Firing strength of the rule (dof) is defined by (14).
11) Defuzzifications are performed using (24).
Based on the parameterization process proposed above for
a singleton FLC, the output of the FLC is given by:
),,,,,( UXoutrulesin ggXFU  (29)
Fig. 5. shows the main components constructing the FLC
generated according to the presented parameterization
methodology.
Fig. 5. Components of a FLC computing algorithm.
IV. ILUSTRATIVE EXAMPLE (INVERTED
PENDULUM PROBLEM)
Consider the problem of balancing an inverted pendulum
on a cart [15] as shown in Fig. 6. for which the dynamic
equation is given by:










 


)(cos
1.1
1.0
3
4
5.0
1.1
)sin(05.0
)cos()sin(8.9
2
2
y
yyu
yy
y

 (30)
where y is the angle of the pendulum with respect to the
vertical line and y is the pendulum angular velocity and u
is the applied control force.
To evaluate the presented methodology, a PD like fuzzy
controller is to be constructed for the non-linear control
system given by (30), using the developed singleton FLC
computing algorithm. We will discuss the regulator problem,
i.e. keeping the inverted pendulum balanced in a vertical
position with reference to different initial positions of )0(y .
A. FLC construction
We will show in a systematic way how the presented
parameterization methodology is applied for computing the
control action u using (29) as follows:
1) The FLC relevant inputs are chosen to be yex 1
and yex  2 with u as the relevant only output,
hence ;2xn and 1un .
2) The active UODs for e and e are chosen to be [ 22 , 
 ]
and [ 44 , 
 ] respectively, and for the output 1u is
chosen to be [ 20,20 ] (i.e. the control signal
boundaries).
3) The input variables are to be partitioned into five
triangular MFs satisfying symmetricity feature with 50%
overlapping. While the output variable is chosen to have
nine equidistant singleton MFs.
Hence:
9;5;5 121
 uxx mmm
And according to (2) for a triangular MF it gives: 3c
and 15in .
Fig. 6. Inverted pendulum on a cart.
4) in is evaluated by (1), and its reduced version (4) gives:









15.005.01
15.005.01
in
with normalization scaling factors evaluated by (27) as:
     20/1;/4;/2 121
 uxx ggg
5) rules reflects the indices of the MFs involved in the
construction of the rule premise, which is identified by
the two inputs 1x and 2x respectively. 25Rn is
calculated by (13), and RB premise is generated by (12)
as:
T
rules 






5
5
123412345123451234512345
555533333444441111122222

6) The consequence out of the rule base is constructed
according to (18), using the summation formula defined
by passino [8], gives the following:
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[out 1 .75 .5 .25 0 | .75 .5 .25 0 -.25|
.5 .25 0 -.25 -.5| .25 0 -.25 -.5 -.75|
0 -.25 -.5 -.75 - 1
T
]
1
where the output MFs are assumed to have the same
partitions of the input MFs, that is identified by:
{ },,,, 54321 mfmfmfmfmf = {NL, NS, Z, PS, PL}
If normalization is used, it will be in the form of:
{-1,-0.5, 0, 0.5, 1}
The rule base table generated by rules and out could
be summarized as shown by Table II.
7) The firing strength
i
 is accomplished using (13), taking
into consideration the choice of a triangular form MFs,
hence (10) is used to evaluate the membership value at
the given measurement points
TABLE II.
RULE-BASE TABLE FOR THE INVERTED PUNDULUM PROBLEM
1cx and 2cx for 1x and 2x respectively, for the
given in .
8) After calculating the vectors  and out , the COG
formula as given by (23) is used for defuzzification,
which will calculate the final crisp value for the control
action 1u at instants k based on a the measurement
values 1cx and 2cx , knowing that )(1 kecx  and
)(2 kecx  .
B. Simulation and results
Complete set of programs are written to simulate the
presented computing algorithm using MATLAB 7.3. The
differential equation for the inverted pendulum problem is
solved by using the fourth order Runge-Kutta algorithm. The
sampling time is taken to be 0.02 seconds.
The closed loop system output which is identified by the
angular position y is examined for different initial
conditions of )0(y . The designed FLC shows a high
stability regulation for the inverted pendulum system over the
entire UOD defined for the controller input variables. The
behavior of the controlled system is summarized by Fig. 7. It
is clearly shown that the effective margin of stability for the
proposed simple FLC controller is capable to control back
the pendulum to its vertical position from different initial
conditions bounded by ]97.42,97.42[)0( y .
VI. CONCLUSION
A full parameterization process for a singleton fuzzy
system is developed. It presents a systematic methodology
for developing a singleton fuzzy logic controller for control
applications. The assumptions made by the parameterization
process is highly simplified the FLC computing algorithm. A
well-known inverted pendulum problem is chosen for
evaluating the capability of the
Fig. 7. Inverted pendulum for different initial conditions using the proposed
singleton FLC.
proposed approach. The obtained result shows the
effectiveness of the developed approach in terms of
simplicity and transparency in setting the solution.
Although, the proposed methodology is tested for
constructing the FLC, a current research now is initiated for
developing a structural design methodology using soft-
computing controller based on the presented
parameterization process. Both FLC structure and FLC
parameters are to be designed and tuned in one single phase
simultaneously.
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Full parameterization process

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