Download International Electrical Engineering Journal (IEEJ) Vol. 5 (2014) No.11, pp. 1632-1638

International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
Performance Analysis of SRM Drives
using Soft Computing Technique based
Fuzzy Logic Controller
1
M.Hari Prabhu, and 2M.Sathya
1
Department of EEE, KNCET, Trichy, Tamilnadu, India
2
Department of ECE, VSB Engineering College, Karur, Tamilnadu, India
[email protected]

Abstract— This project is based on the Performance analysis of
Switched Reluctance Motor (SRM) drives using Z-Source
Inverter with the soft computing based Fuzzy logic rule base of
control rule reduction with the output scaling factor(SF)
Self-Tuning Fuzzy Controller (STFC) mechanism are proposed.
Fuzzy logic speed controller for SRM drives can produce
smooth torque and improve the system performance. The
output SF of the controller can be tuned continuously by a gain
updating factor, whose value is derived from fuzzy logic, with
the plant error and error change ratio as input variables. The
result shows the effective control rule reduction of the proposed
methods which can be carried out on a four-phase 8/6 pole SRM
are given to show the very good stability and robustness against
speed and load variations over a wide range of operating
conditions.
Index Terms—Fuzzy logic controller, scaling factor (SF),
switched reluctance motor (SRM), Z-Source Inverter.
I. INTRODUCTION
The Switched Reluctance motor is an electromechanical
device that converts electrical energy into mechanical energy.
With the advances in power electronics and high-tech control
techniques, as well as the development of high speed
microcontrollers with powerful computation capability,
switched reluctance motor (SRM) drives are under
consideration in various applications requiring high
performance,
As the development of high speed microcontrollers with
powerful computation capability, switched reluctance motor
(SRM) drives are under consideration in various applications
requiring high performance. SRMs inherently feature
numerous merits like simple and rugged structure, being
maintenance free, high torque–inertia ratio, fault-tolerance
robustness and reliability, high efficiency over a wide range
of speeds, etc. The requirements for variable-speed SRM
drives include good dynamic and steady-state responses,
minimum torque ripple, low-speed oscillation, and
robustness. However, due to the heavy nonlinearity of the
electromagnetic property and the coupling relationships
among flux linkage, torque, and rotor position, it is not easy
for an SRM to get satisfactory control characteristics.
Therefore, new structure designs [1], high performance
magnetic cores [2]. Intelligent control techniques such as
fuzzy logic control (FLC), neural network control, or genetic
algorithm may allow better performance. Intelligent control
approaches try to imitate and learn the experience of the
human expert to get satisfactory performance for the
controlled plant [3]. One of the most powerful tools that can
translate linguistic control rules into practical operation
mechanism is the FLC. It has been shown that fuzzy control
can reduce hardware and cost and provide better performance
than the classical PI, PD, or PID controllers [5]. Recently,
fuzzy control theory has been widely studied, and various
types of fuzzy controllers have also been proposed for the
SRM to improve the drive performance further [3],[6]–
[8].Performance of the FLC are scaling factor (SF) tuning,
rule base modification, inference mechanism improvement,
and membership function redefinition and shifting. The
initial parameters and scaling gains of the controller are
optimized by the genetic algorithm to minimize overshoot,
settling time, and rising time. An adaptive fuzzy controller
for torque-ripple minimization is presented by Mir et al. [4].
An adaptive FLC with scaling gain tuning is proposed in [7].
The universe of discourse (UOD) of the fuzzy sets can be
tuned by altering the scaling gain according to the input
variables. This significantly improves the system transient
and steady-state responses. Koblara proposed a fuzzy logic
speed controller for SRM drives [8].Fuzzy controller can
produce smooth torque and improve the system
performance.Z-source inverter system for adjustable speed
drives (ASD)[10]. It can produce any desired output ac
voltage, even greater than the line voltage. It reduces line
1632
Prabhu, and Sathya
Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller
International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
harmonics,and extends output voltage range. ZSI is designed
suitable for wind power conversion system [9]. The main
challenge in wind power system to maintain a constant
voltage at the output with unpredictable variation in wind
speed,is suitably taken care in steady state through
buck-boost-capability of (ZSI). The most concerning
disturbances affecting the quality of the power in the
distribution system are voltage sag/swell [11]. This project
[13] deals with study and analysis of three level diode
clamped MOSFET based inverters and its applications in
industries. The main purpose of the paper is to study and
implement 3 level diode clamped inverter using MOSFET’s,
the PWM signals used to switch these MOSFET’s has been
generated using PWM IC HEF4752VP. However, the output
voltage is smoother with a three level converter, these results
in smaller harmonics.
II. SRM DRIVE SYSTEM
With the advances in power electronics and high-tech control
techniques, as well as the development of high speed
microcontrollers with powerful computation capability,
switched reluctance motor (SRM) drives are under
consideration in various applications requiring high
performance, such as servomotor drives, electric vehicle
propulsion, jet engine starter–generators, etc. SRMs
inherently feature numerous merits like simple and rugged
structure, being maintenance free, high torque–inertia ratio,
fault-tolerance robustness and reliability, high efficiency
over a wide range of speeds, the capability to run in
abominable circumstances, etc. Generally the Switched
Reluctance drive controller requires rotor position feedback,
because excitations of the Switched Reluctance motor phases
need to be synchronized with the rotor angle. Position sensors
are commonly employed to obtain rotor position
measurements, however in many systems advantages can be
found in eliminating these sensors. These benefits include the
elimination of electrical connections to the sensors, reduced
size, low maintenance, and insusceptibility to environmental
factors.
The Switched reluctance motor (SRM) drives are simpler in
construction compared to induction and synchronous
machines. The effects of machine operation and its salient
features are inferred from the torque expression. The torque
expression requires a relationship between machine flux
linkages or inductance and the rotor position. The switched
reluctance motor (SRM) is a doubly salient machine in which
torque is produced by the tendency of the rotor to move to a
position where the inductance of the excited winding is
maximized. The rotor is aligned whenever diametrically
opposite stator poles are excited. In a magnetic Circuit, the
rotating member prefers to come to the minimum reluctance
position at the instance of excitation. The simple mechanical
construction is one of the main attractive features because it
Prabhu, and Sathya
has no windings or permanent magnets on the rotor and thus,
high efficiency and manufacturing cost appears to be lower
compared to other motor types. The switched reluctance
motor (SRM) is simple, low cost, fault tolerant, and has
simple converter and control requirements. The motor is
suitable for many variable-speed and servo-type applications
and is gaining increasing attention in the motor drive
industry. One of the main disadvantages of the SRM is its
higher torque ripple compared to other electrical machines.
Various techniques have been developed for torque-ripple
minimization of SRM’s. Methods, such as current profiling,
overlapping conduction of different phases, and linearized
feedback control were used in this research. Some of these
methods use off-line data collected from the machine during
the static conditions, which change during the motor
operation due to changes in the motor parameters. Therefore,
to maintain a constant torque, the current profile has to be
adjusted continuously during phase conduction. The total
torque of the machine is the sum of the torque produced by
the independent motor phases. In order to have a continuous
torque, smooth commutation from one phase to another is
needed. The torque pulsation during the overlap period can
be quite significant if the commutation process is not smooth.
This effect becomes more significant at higher speeds, when
the commutation takes place over a large angle of the rotor,
and the phases start to demagnetize before they reach the
aligned position. If the phases are not demagnetized before
the aligned position, the phases conduct in the generating
region producing negative torque, thereby reducing the
efficiency of the motor. Also, the torque production capacity
of the drive is decreased as the speed is increased. Z-source
inverter system for adjustable speed drives (ASD) can
produce any desired output ac voltage, even greater than the
line voltage. ZSI is designed suitable for wind power
conversion system, it reduces line harmonics, and extends
output voltage range. The main challenge in wind power
system to maintain a constant voltage at the output with
unpredictable variation in wind speed, is suitably taken care
in steady state through buck-boost capability of Z-source
inverter. The requirements for variable-speed SRM drives
include good dynamic and steady-state responses, minimum
torque ripple, low-speed oscillation, and robustness.
However, due to the heavy nonlinearity of the
electromagnetic property and the coupling relationships
among flux linkage, torque, and rotor position, it is not easy
for an SRM to get satisfactory control characteristics. Based
on the exact mathematical model and system operator’s
experience, the PI, PD, or PID controller could compensate
system variations very efficiently via the appropriate tuning
of its dominant parameters. However, the performance may
significantly deteriorate when the operating condition is
altered. When the exact analytical model of the controlled
system is uncertain or difficult to be characterized, intelligent
control techniques such as fuzzy logic control (FLC), neural
network control, or genetic algorithm may allow better
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Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller
International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
performance. Intelligent control approaches try to imitate and
learn the experience of the human expert to get satisfactory
performance for the controlled plant. One of the most
powerful tools that can translate linguistic control rules into
practical operation mechanism is the FLC. The FLC has been
found particularly suitable for controller design when the
plant is difficult to model mathematically due to its
complexity, nonlinearity, and/or imprecision. Hence, the
FLC is widely applied in a considerable variety of
engineering fields today because of its adaptability and
effectiveness. The FLC architecture approximates the way of
expert operation sensitivity; this makes it attractive and easy
to incorporate experimental rules.
Recently, fuzzy control theory has been widely studied,
and various types of fuzzy controllers for the SRM to
improve the drive performance. The main techniques utilized
to enhance the self-adaptability and performance of the FLC
are scaling factor (SF) tuning, rule base modification,
inference mechanism improvement, and membership
function redefinition and shifting. Among these techniques,
SF tuning is the most used approach, and it has a significant
impact on the performance of an FLC. The position estimator
uses fuzzy logic based modeling, estimation, and prediction
to provide a high system reliability against feedback signal
noise and error. However, the control of the SRM is not an
easy task. The motor's double salient structure makes its
magnetic characteristics highly nonlinear. The motor flux
linkage appears to be a nonlinear function of stator currents
as well as rotor position, as does the generated electric torque.
The advantages of Fuzzy logic controller are such as
simplicity of control through soft computing technique and
the possibility to design without knowing the exact
mathematical model of the process. This control technique
relies based on the human capability to understand system
behavior and is based on qualitative control rules. Since
Fuzzy control is based on experimental rules, it makes easy
application of non-linear control. Fuzzy controller can
produce smooth torque and improve the system performance.
controlled switch (e.g. power transistor or IGBT) is necessary
and sufficient to supply an unidirectional current during
appropriate intervals of rotor rotation. Fig. 1 shows the
typical cross sectional arrangement for a 4-phase SRM
having Ns=8 stator and Nr=6 rotor poles (‘8/6-SRM’).
Fig. 1 Arrangement of a 4-phase 8/6-SRM with switching circuit
Fig. 3 Basic Components of Switched Reluctance Machine drive
A SRM has salient poles on both stator and rotor. Its
magnetic core consists of laminated steel. Each stator pole
has a simple concentrated winding and there are no
conductors of any kind on the rotor. Diametrically opposite
windings are connected together either as a pair or in groups
to form motor phases. For each phase a circuit with a single
It is derived from the actual current value, rotor position
angle and the reference value of torque or speed, the control
unit determines the switching signals for the active switches
(e.g. power transistors, IGBTs etc.) in the converter. The
electromagnetic torque is independent from the current
direction, thus an unipolar converter is sufficient. It is usually
Fig. 2 (a) Cross-sectional profile and (b) equivalent circuit of 8/6-pole SRM
The equivalent circuit can be represented by a resistance R in
series with an inductance L (i, θ), which is a function of rotor
position θ and excitation current i. From Fig. 1(b), the phase
voltage can be expressed by
dλ(i,θ)
V(t)=R.i(t)+
With
(1)
dt
λ(i,θ) = L(i,θ)i
(2)
where λ is the flux linkage, which is dependent on i and θ.
v(t), i(t), and L(i, θ) are the instantaneous voltage across the
excited phase winding, the excitation current, and the
self-inductance, respectively.
The mechanical torque of the rotor can be expressed as
Tmec = Te - Bω - J
dω
dt
(3)
where J, B, and Tmec stand for the machine’s moment
inertia, friction coefficient, and mechanical torque,
respectively.
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Prabhu, and Sathya
Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller
International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
supplied by a constant dc-voltage Vdc. The optimum
performance of an SRM depends on the appropriate
positioning of the currents relative to the rotor angular
position. The motor controller selects the turn-off angles Θoff
in such a way that the residual magnetic flux in the
commutating phase decays to zero before the negative dL/dΘ
region is reached in the motoring mode of operation. There
exist in general two modes of control the phase current which
will be discussed in point 3.7. In the low speed operation
mode, torque is maintained constant by keeping the phase
current constant. The independent stator phases contribute, in
succession, to maintain continuous torque production in the
direction of rotation. During commutation each of the two
adjacent conducting phases produce torques which are
additive. Torque ripple is inversely related to the smoothness
of current transfer between phases, and it is possible to
minimize the ripple during transition by controlling currents
in the overlapping phases.
If the current is controlled only in the incoming but
not outgoing phase, the sum of the air gap torques contributed
by the outgoing and incoming phases during this interval is
usually not constant. The resulting torque has a trough during
this commutation interval that increases the torque ripple.
time. It grabs plant output y(t), compares it to the desired
input r(t), and then decides what the plant input (or controller
output) u(t) should be to assure the requested performance.
The inputs and outputs are crisp. The fuzzification block
converts the crisp inputs to fuzzy sets, and the defuzzification
block returns these fuzzy conclusions back into the crisp
outputs. Inference engine using if-then type fuzzy rules
converts the fuzzy input to the fuzzy output.
(4)
III. FUZZY LOGIC CONTROLLER DESIGN
In this part, the fuzzy control fundamentals will be outlined
first, and then, the key point of self-tuning PI-like fuzzy
controller (STFC) will be briefly reviewed. Afterward, the
modified design of the proposed STFC will be described in
detail.
Fig. 5 Block diagram of the proposed FLC with reduction of the control rule
This is a simple but robust model-independent
self-tuning mechanism for FLCs with the most important
feature that it depends neither on the process being controlled
nor on the controller used. Twenty-five fuzzy rules are
needed for deriving controller output Δu and α, respectively.
This project focuses on, first, the reduction of the number of
fuzzy rules for deriving α and, second, the simplification of
the memory requirement and computational complexity of
the designed controller. The self-adjusting mechanism of the
proposed control rule reduction of fuzzy controllers is
described as follows:
1. Variation Effect of Input and Output SFs: SF modulation is
one of the most employed solutions to enhance the
performance of a fuzzy controller. The effect of SF
adjustment is equivalent to extending or shrinking the actual
UOD of the input and output variables.
Fig. 4 Basic structure of a fuzzy logic control system
A basic FLC system structure, which consists of the
knowledge base, the inference mechanism, the fuzzification
interface, and the defuzzification interface, is shown in Fig. 3.
Essentially, the fuzzy controller can be viewed as an artificial
decision maker that operates in a closed-loop system in real
1635
Prabhu, and Sathya
Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller
International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
2. Self-Tuning Mechanism: The systemic methods for gain
tuning to obtain the optimal response because the
determination of the optimal values of the adjustable
parameters requires the knowledge of a precise model of the
plant. The design guidelines are described as follows:
Step1. The proper initial values of Ge, GΔe, and GΔu can be
obtained by (3.4) for control rule reduction. Choosing Ge
GΔe and GΔu to cover the whole normalized domain [−1, 1]
or interval [emin, emax]. An appropriate initial operating
condition is obtained when a good transient response is
achieved.
1
Ge =
(or
1
𝜔𝑒𝑟𝑟,𝑚𝑎𝑥
|𝜔𝑒𝑟𝑟,𝑚𝑖𝑛|
1
GΔe =
, GΔu = Δumax
Δ𝜔𝑒𝑟𝑟,𝑚𝑎𝑥
)
(5)
(6)
Step2. In this step, the controller output and updating factor
can be expressed by (7) and (8), respectively, fαindex is a
nonlinear function and kΔu is the scaling constant of GΔu.
Here, GΔu is set kΔu times greater than that obtained in
Step1. The determination of kΔu is empirical. At the same
time, the output SF can be fine-tuned by altering the value of
αindex to achieve a relatively small but satisfactory output
and guarantee a faster response with relatively small
overshoot.
Δu(k) = αindex(k) (kΔuGΔu) ΔuN
(7)
αindex(k) = fαindex (e(k), Δe(k))
(8)
Step3. Fine-tune the rules for αindex based on the required
response and the particular considerations for deriving the
control rules if necessary. Hence, the proper αindex is
fine-tuned from various operating conditions. The UOD in all
membership functions of the controller inputs, i.e., e and Δe,
and output, i.e., Δu, are defined on the normalised domain
with the gain updating factor α of (4x4=16) rule base over the
interval [0,1], as shown in Fig.5 The linguistic values NB,
ZE, PS, and PB stands for negative big, zero, positive small,
and positive big, respectively.
Fig. 6 Membership Functions of e, Δe, and Δu with gain updating factor αr of
4x4 rule base
TABLE I RULE BASE (4X4) FOR DERIVING OUTPUT VARIABLE ΔU
Here, the focus is on the reduction of rule numbers for
deriving updating factor αr. The proposed frame of the
reduced rule base for deriving αr is shown in Table 1. In this
proposed scheme, although there are 16 control rules. Each
time, only five control rules are used to derive αr when the
chosen subroutine is executed. Therefore the dynamic
behavior of motor step response of control rule reduction will
attain its steady state very quickly by using (4x4=16) rules.
This result shows that very good stability and robustness
against speed and load variations and also the motor step
response of Control rule reduction will attain its steady state
very quickly.
IV. SOFTWARE IMPLEMENTATION
Δe
o/p
NB
ZE
PS
PB
NB
NB
NS
NS
ZE
ZE
NS
ZE
PS
PS
PS
NS
PS
PS
PB
PB
ZE
PS
PB
PB
e
Prabhu, and Sathya
Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller
1636
International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
algorithms of torque iterative learning control (TILC)
and energy iterative learning control (EILC) are run to
minimize the torque ripple and energy conversion loss
by regulating the incremental turn-on and turn-off
angles (Δθon,Δθoff) and the duty cycle (D) to enhance
the driving performance. The commutation logic
controller is used to derive and determine the phase
commutation moment according to the rotor position,
excitation turn-on angle, and turn-off angle.
Fig. 9 Dynamic behavior of motor step response of the simplified rule
Fig. 7 Simulation Block of Fuzzy Logic Controller
base
The fuzzy Logic speed controller receives the speed
error signal and converts it into four-phase current commands
that will be sent to the current controller. The linguistic
values NB, NS, ZE, PS, and PB stand for negative big,
negative small, zero, positive small, and positive big,
respectively.
The table shows the various set speed and settling time of
Fuzzy Logic Speed Controller. Therefore the settling time of
control rule reduction should be improved better when
compared to the conventional method of more number of
Fig. 8 Simulation Block of the Proposed System
The block diagram of simulation block consists of
Z-Source Inverter (ZSI), fuzzy Logic speed controller,
four-phase 8/6-pole Switched Reluctance Motor (SRM) and
the power inverter with gate driver circuit. The fuzzy speed
controller receives the speed error signal and converts it into
four-phase current commands that will be sent to the current
controller. The actual current, sensed by the Hall-effect
sensor, is compared with the current command to obtain the
current error. According to the error value, the pulse width
modulation gating signals of insulated-gate bipolar
transistors in an asymmetric half-bridge power inverter are
generated by the current controller. The gating signals drive
the power inverter through the photo coupler isolation. If any
voltage variations in the source, at that time Z-source Inverter
(ZSI) can be used to boost up the required voltage to maintain
the constant voltage at the input of the SRM drives. It also
improves the reliability, reduces line harmonics and extends
output voltage range. With the inputs of actual speed, speed
errors, current, and rotor position estimation consists of both
rules are used in the Fuzzy Logic Controller.
Sl.No
Speed (RPM)
Fuzzy Controller
(Settling Time)
1
500
0.067
2
750
0.071
3
1000
0.078
4
1250
0.081
5
1500
0.086
TABLE II Study of set Speed and settling Time
In this project we have studied about different speed
control of SRM Drives for various speeds. The steady state
operation and its various speeds settling time period of SRM
drives are studied. The basic definition and terminology of
fuzzy logic and fuzzy set were studied. This project
introduces a design of 4x4 simplified Self-tuning Fuzzy
Logic Controller.
Thus the results of the proposed control rule
reduction, shows very good stability and robustness against
speed and load variations over a wide range of operating
conditions and the performance of the proposed control rule
reduction scheme can also be improved. Z-source Inverter
can be used to boost up the required voltage to maintain the
voltage constant at the input of the SRM drives. It reduces
line harmonics, improves reliability, and extends output
voltage range. At the same time, the output SF can be
fine-tuned by altering the value of αindex to achieve a
relatively small but satisfactory output and guarantee a faster
response with relatively small overshoot. Therefore the
1637
Prabhu, and Sathya
Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller
International Electrical Engineering Journal (IEEJ)
Vol. 5 (2014) No.11, pp. 1632-1638
ISSN 2078-2365
http://www.ieejournal.com/
dynamic behavior of motor step response of control rule
reduction will attain its steady state very quickly.
V CONCLUSION
In this project, based on the reduction of control rule base,
thus the four-phase 8/6- pole Switched Reluctance Motor
(SRM) drives using Z-Source Inverter with the modified rule
base of 4x4 using Fuzzy Logic Controller (FLC) with the
output scaling factor (SF) self-tuning mechanism by altering
a gain updating factor has been devised. The simplified rule
bases are designed to simplify the program complexity of the
controller by reducing the number of fuzzy sets of the
membership functions without losing the system
performance and stability using the adjustable controller
gain. Fuzzy controller can produce smooth torque and
improve the system performance. Z-source Inverter is also
proposed, which can be used to boost up the required voltage
to maintain the voltage constant at the input of the SRM
drives. It reduces line harmonics, improves reliability, and
extends output voltage range. Therefore the dynamic
behavior of motor step response will attain its steady state
very quickly by using 16 rules. Thus the results of the
proposed control rule reduction of FLC, shows very good
stability and robustness against speed and load variations
over a wide range of operating conditions.
for Motor Drives,” IEEE Trans. Power Electron., vol.20, no.
4,pp.857-863, July 2005.
[11] M.Balamurugan, T.S. Sivakumaran, and M.Aishwariya Devi, “Voltage
Sag/Swell Compensation Using Z-source Inverter DVR based on
FUZZY Controller,” presented at IEEE International Conference on
Emerging Trends in Computing, Communication and Nanotechnology,
pp.648-653 ,2013.
[12] Anju, Rajasekaran, “Design and Performance Analysis of High Speed
Switched Reluctance Motor Drive for Various Industrial Applications‖
published at International Electrical Engineering Journal (IEEJ),Vol. 5
(2014) No. 1, pp. 1171-1178 ISSN 2078- 2365.
[13] Dnyaneshwar D. Khairnar and V. M. Deshmukh, “Performance
Analysis of Diode Clamped 3 Level MOSFET Based Inverter published
at International Electrical Engineering Journal (IEEJ), Vol. 5 (2014)
No.7, pp. 1484-1489 ISSN 2078-2365.
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Performance Analysis of SRM Drives using Soft Computing Technique based Fuzzy Logic Controller