Hybrid controller design using gain scheduling approach for compressor systems

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 3, June 2022, pp. 3051~3060
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i3.pp3051-3060  3051
Journal homepage: http://ijece.iaescore.com
Hybrid controller design using gain scheduling approach for
compressor systems
Divya Muddenahalli Narasimhaiah1,2
, Chikkajala Krishnappa Narayanappa3
, Gangadharaiah
Soralamavu Lakshmaiah4
1
VTU Research Centre, M.S. Ramaiah Institute of Technology, Visvesvaraya Technological University,
Belagavi, India
2
School of Electronics and Communication Engineering, REVA University, Bengaluru, India
3
Department of Medical Electronics, M. S. Ramaiah Institute of Technology, Bengaluru, India
4
Department of Electronics and Communication, M. S. Ramaiah Institute of Technology, Bengaluru, India
Article Info ABSTRACT
Article history:
Received May 20, 2021
Revised Dec 24, 2021
Accepted Jan 19, 2022
The automatic control system plays a crucial role in industries for
controlling the process operations. The automatic control system provides a
safe and proper controlling mechanism to avoid environmental and quality
problems. The control system controls pressure flow, mass flow, speed
control, and other process metrics and solves robustness and stability issues.
In this manuscript, The Hybrid controller approach like proportional integral
(PI) and proportional derivative (PD) based fuzzy logic controller (FLC)
using with and without gain scheduling approach is modeled for the
compressor to improve the robustness and error response control
mechanism. The PI/PD-based FLC system includes step input function, the
PI/PD controller, FLC with a closed-loop mechanism, and gain scheduler.
The error signals and control response outputs are analyzed in detail for
PI/PD-based FLC’s and compared with conventional PD/PID controllers.
The PD-based FLC with the Gain scheduling approach consumes less
overshoot time of 74% than the PD-based FLC without gain scheduling
approach. The PD-based FLC with the gain scheduling approach produces
less error response in terms of 7.9% in integral time absolute error (ITAE),
7.4% in integral absolute error (IAE), and 16% in integral square error (ISE)
than PD based FLC without gain scheduling approach.
Keywords:
Compressor
Fuzzy logic controller
Gain scheduling
Output response
Plant
Proportional integral derivative
This is an open access article under the CC BY-SA license.
Corresponding Author:
Divya Muddenahalli Narasimhaiah
VTU Research Centre, M. S. Ramaiah Institute of Technology, Visvesvaraya Technological University
Belagavi, Karnataka, India
Email: draophd@gmail.com
1. INTRODUCTION
Compressor systems are used to enlarge the air pressure (gas) by minimizing its volume
mechanically. The frequently compressed gas is air and oxygen, carbon dioxide, natural gas, and other
industrial gases. These compression systems used in many applications include pipeline transportation,
vehicular propulsion for military and civilian use, electrical power generation, aircraft transportation, and
other thermal plant industries. The compressor has a significant drawback with surge control and rotating
stall problems, which cause instabilities in mass flow and pressure conditions [1]. The proportional integral
derivative (PID) controller and fuzzy logic-based PI controllers overcome the operational surge problems in
the centrifugal compressor; the recycle (surge) values are controlled with PID and fuzzy-based PI controllers
to overcome the instability problems in the compressor system [2]. Different controlling techniques are

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available as an intelligent controller in a hybrid braking system and electric motors, DC motors for
controlling purposes [3], [4]. The fault detection and isolation of surge are achieved by fuzzy logic modeling
for industrial centrifugal compressors and gas turbine modules [5], [6]. The different controllers, like PID,
fuzzy logic controller (FLC), PID based fuzzy controllers, are used in many applications. The electronic
throttle control (ETC) valve is optimized using PID type fuzzy factors with a genetic algorithm [7], and FLC
achieves the air conditioning control for energy saving [8]. The ETC for autonomous vehicles using
fractional-PI controllers [9], overshoot suppression for ETC using fuzzy based PI controller [10], level and
pressure process control are achieved by fuzzy-PID controllers [11].
The non-linear and highly uncertain system control is a complex and challenging task for designers.
These non-linear control systems are not modeled systematically, forcing unknown parameters to affect the
overall system performance. The fuzzy logic methods are introduced, which are functional and adequate to
solve the non-linearity system problems. The Takagi-Sugeno (T-S) fuzzy model is introduced in 1985, which
elaborates the non-linear system with few linear submodules using a fuzzy rule set. The non-linear system is
modeled with the help of these linear submodules, which are associated with a set of rules and are fuzzily
blended. The T-S model provides better-controlling capabilities by utilizing the possible linear control
methods. Advanced fuzzy dynamic systems have been developed to control the robust adaptive multiple
non-linear system models [12]–[14].
Automatic control devices are essential and used in most industries for process control operations.
Automatic process control is a significant part of industrial systems, which provides safe and proper control
to avoid quality and environmental problems. Many processing operations in industries include thermal
power plant, petroleum, and chemical industry for controlling mass flow, pressure flow, temperature, and
other similar performance metrics. In general, automatic process control has a typical controller, control
elements, process operation, and feedback mechanisms. The overview of the automatic control process
system is represented in Figure 1. It is used to maintain the actual value by calculating the existing value with
the actual value and obtaining the difference to start the action to minimize the error. The typical controller
provides the controlling mechanism of the process. The control elements receive the input from the controller
and perform the corresponding operation. The process is also known as a plant with an industrial compressor,
gas turbines, thermal power plant, and many more. The process generates the outputs, measurable and
converted to one form variable to the corresponding variable by measurement operation.
The error system (e) finds the difference between the reference valve (i) and the actual value (a).
The control system performance depends on the error signal, and it is represented as e (t)=i(t)–a(t). The main
aim of the controller is to reduce the error signal to exactly zero concerning time. The performance
parameters of the control system have a steady-state, stability, and transient response. The transient response
is calculated using rise time, delay time, peak time, settling time, overshoot, and error characteristics.
Figure 1. An overview of automatic process control system
Most controllers are designed for gas turbines, ETC applications and not particularly for the
compressor system. Very few approaches are considered for the compressor system with lags of performance
constraints like robustness, Stability, control response, and error responses issues. The proposed approach is
used to improve the aforementioned issues, using a hybrid control mechanism and suitable to use further in a
particular compressor system. In this manuscript, an efficient PI and PD-based fuzzy logic controller is
modeled for compressor applications. This section explains the review of existing approaches towards
different controllers for different applications with limitations. Section 2 describes the controlling strategies
using PI/PD based FLC with gain scheduling. The discussion of simulation results with performance metrics
realization is carried in Section 3. Finally, the overall work is concluded in Section 4 with future work.
This section elaborates on the existing approaches of the different processes using different
controlling techniques with their limitations. Ahmad et al. [15] present the mixed fuzzy logic control method
to track a flexible-joint manipulator’s trajectory and vibration control. The tip angular position control is

Int J Elec & Comp Eng ISSN: 2088-8708 
Hybrid controller design using gain scheduling approach for … (Divya Muddenahalli Narasimhaiah)
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calculated using PD-based FLC. Additionally, the non‐collocated PD-based FLC module is modeled for
vibration reduction of the flexible joint system with a suitable input scheme. The reduction of deflection
angle for vibration and time response of angular tip position are analyzed with suitable parameters. Kozák
[16] elaborates on the different controlling techniques from PID to model predictive control (MPC). The PID
controller’s usage in today and the future, PID tuning methods, gain scheduling methods, fuzzy-based PID
methods, advanced neural network-based PID method, and hardware realization of tuning methods and their
drawbacks are explained. The MPC modeling, classification, and analysis with field programmable gate array
(FPGA) hardware are discussed. Liying et al. [17] examine the electronic throttle valve (ETC) compensation
control method to improve the tracking realization. The non-linear compensation control system includes
PI-controller, ETC, and the pulse width modulation (PWM) position system.
Pan et al. [18] present the adaptive fuzzy H∞
tracking control (AFHC) model for guaranteed stable
tracking using PD-FLC than the sliding mode control (SMC) approach. The work analyzes the tracking
performance of two controllers. The existing AFHC methods are overcome with the AFHC module
incorporating the SMC. The neuro-fuzzy controller is designed for heavy-duty gas turbine plants by Iqbal et
al. [19] to overcome load fluctuations and set-point variations. The response of different modules is analyzed
using FLC and neural network controller. These controllers are acts as a speed controller in the turbine plant.
Pezzetti et al. [20] present different controlling methods for the helium cryogenic warm compressor station to
improve the transient and setup requirements. The control techniques include PID, FLC, and internal model
control (IMC) techniques are used in cryogenic systems to check the high and low-pressure control
variations. The ETC module is designed using the fuzzy sliding-mode controller (SMC) by Bai et al. [21],
which includes mathematical modeling of regular and fuzzy sliding mode controller of ETC and simulation
analysis of the plate angle and desired angle of the valve plate. Similarly, Yadav et al. [22] designed an
Intelligent ETC system using different controllers like PID, SMC-based PD, and FLC. The fuzzy based PID
gives better Overshoot and settling time than other approaches.
The optimal speed control for Hybrid electric vehicles is designed by Saeed et al. [23] using
different controllers like PID, pole placement (PP), state observer-based (OB), and linear-quadratic regular
(LQR) based controllers. Electric vehicles analyze the speed control process using stability factors,
observability, and controllability factors. Chao et al. [24] describe the comparative analysis of fuzzy-PID
over conventional PID controllers with constraints improvements. The optimized PID-based FLC for the
higher-order control system is designed by Bharti et al. [25]. The work analyzes the response of the plant
system using different controllers and also tabulate the performance metrics. The fractional-PID based FLC
is designed by Barbosa et al. [26]. This hybrid controller is fine-tuned with the help of a genetic algorithm to
improve the performance of the non-linear system. Somwanshi et al. [27] explain the speed control of DC
motor in LabView and analyze the fuzzy-PID and typical PID controller results with response time.
Prayitno et al. [28] explain the fuzzy gain scheduling approach for the position of the drone system
using a PID controller. The PID controller acts as the central controller of the drone system, which controls
the throttle, roll, and pitch. The three PID controllers are tuned by the scheduler and calculated by the fuzzy
logic module. The control response, square shape experiments, control signal waveforms (throttle, roll, and
pitch) are discussed in detail for the drone system. Shah et al. [29] present the PID-based temperature control
system are implemented for an electric kettle. The temperature control system includes PID and bang-bang
controllers, followed by a driver circuit (solid-state relay), heater, and electric kettle. The temperature control
system is implemented on microcontroller and connected to an electric kettle via computer. The water
temperature response inside the electric kettle at different degrees celsius are analyzed using PID controller.
Prabhakar et al. [30] present the non-linear adaptive cruise control system using fuzzy PD plus I controller to
provide set-point tracking performance with adaptive features. The fuzzy-based PD controller provides better
servo performance and solves the regulatory issues by integrating the integral (I) controller in the cruise
control system. Khamari et al. [31] present the hybrid fuzzy PD-PI controller with a modified moth swarm
algorithm (MMSA) is modeled for an electric vehicle system. The MMSA is used to provide the solution for
distributed power generation system with frequency control features. The present work better frequency
control mechanism than the existing similar approach for distributed power generation systems. Tran et al.
[32] describe the fuzzy gain scheduling (FGS) control system for remote control hovercraft. The FCS
approach is used for tuning the PID controller by scheduling the controlled gains. The PID controller outputs
are used in hovercraft motion to analyze the surge, sway, and steering yaw control responses. Zhang et al.
[33] present a reconfigurable adaptive control technique for a cabin pressure control system (CPCS). The
control technique is used to compensate for the faults in sensors and actuators in CPCS. The control system
works with a closed-loop structure, improving the stability and updating the online faults without using the
identification process. Urrea et al. [34] discuss the different control strategies for robotic arm systems using a
multi-level inverter. The control strategies like the gain scheduling approach with trenches and interpolation,
fuzzy logic with adaptive control mechanisms are used to find a robotic arm's position with high-frequency
reduction.

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2. PROPOSED CONTROLLING STRATEGIES
The conventional controller’s overview, fuzzy logic controller design model, and hybrid controller
strategies with a gain scheduling approach are discussed in this section in detail.
2.1. Conventional controllers
The conventional PID controllers are mainly used in many industrial controlling applications
because of their robust performance, smooth operation, and simplicity. The PD/PID controllers are suitable
and adopted in lower and second-order control systems. For tuning the P, I, and D constant values, the
Ziegler-Nichols (Z-N) method is used. The tuned values are considered for PD/PID controllers. PD and PID
are considered for process control operations by tuning the KP, KI, and KD constants in the design work. The
typical compressor system (process) and its transfer function are considered for different controller designs to
improve the efficiency of the process. The transfer function of the compressor plant (process) is represented
in (1).
𝐺(𝑠) =
1
𝑠3+3𝑠2+3𝑠+1
(1)
The fuzzy logic controller using the gain scheduling approach is used to improve the controller performance
parameters. The hybrid approach of PI and PD-based FLC controller is designed in this next section to
overcome the conventional controller’s control parameters.
2.2. Fuzzy logic controller (FLC)
Fuzzy logic is a logical system and is controlled by fuzzy control. the fuzzy logic controller (FLC) is
one of the best solutions for industrial control applications. The FLC achieves better performance metrics
than conventional PID controllers. The FLC has many advantages over other controllers includes easy to
understand; it can be tolerant of the indistinct data values. The FLC has five significant components along
with the plant, namely, fuzzification, knowledgebase (Database), evaluation, ruleset, and defuzzification are
represented in Figure 2. Fuzzification is a process of converting crisp (absolute) value to linguistic (fuzzy)
value. The fuzzification receives the PI/PID controller’s data values as crisp inputs into fuzzy systems
(variables). The fuzzy variables are noticed by linguistic values, which are subsets or fuzzy sets. The fuzzy
sets are addressed by membership functions, including oversized, small, low, and high. The rule evaluation
makes the decision logic to control the fuzzy rule stored by the rule base. The rule base is essential for
process performance improvements. The basic fuzzy set with AND, OR, and NOT are used to evaluate fuzzy
rules. Defuzzification is the reverse operation of fuzzification, which is used to convert fuzzy values to crisp
values. The knowledge-base has been used to stores the fuzzy set membership functions of the fuzzifier and
defuzzifier.
Figure 2. FLC configuration with process
2.2.1. Fuzzy variables
The fuzzy interface system (FIS) is designed for FLC using Mamdani in Simulink. The first and
second input variables of the FLC is defined as (2) and (3):
𝐸(𝑖) = 𝑆𝑟𝑒𝑓 − 𝐺(𝑠) (2)
𝐶𝐸(𝑖) = 𝐸(𝑖) − 𝐸(𝑖 − 1) (3)

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where i=1 to 7, Sref is Step input, G(s) transfer function output, E(i-1) is previous error input with derivation
(D) or integration (I) for PD-FLC and PD-FLC system. The difference between the step input and transfer
function Plant output gives the error value of the controller.
2.2.2. Membership functions
The error (E), change in error (CE), and FLC output (O) are normalized to the display range [-1 to 1]
using a set of fuzzy membership functions for the given universe of discourse. The E and CE use a
combination of triangular shaped and Trapezoidal shaped membership functions. In contrast, FLC output (O)
uses only triangular-shaped membership functions. The hybrid combination of the membership function
improves the robustness, control response, less steady-state Error, and less overshoot time for the given
system. The rule-base is activated effectively based on the FLC inputs within the universe of discourse. The
seven fuzzy (linguistic) variables (FV) are used as E and CE, which includes negative big (NB), positive big
(PB), negative medium (NM), positive medium (PM), negative small (NS), positive small (PS), and zero
(ZO). The decision logic is evaluated with the human knowledge base into a fuzzy set by fuzzy rules. The
FIS has the membership function, which includes E, CE are input variables, and output (O) variables are
represented in Figures 3(a) to (c). The 3D surface viewpoint of the PI and PD-FLC controller’s E and CE
membership function is illustrated in Figure 3(d).
2.2.3. Fuzzy rules
To design the rule base, The fuzzy rules are categorized into seven basic fuzzy sets and are: Error
input E(i): {NB, NM, NS, ZO, PS, PM, PB}, Change in error input CE(i)={NB, NM, NS, ZO, PS, PM, PB}
and FLC output O(i)={NB, NM, NS, ZO, PS, PM, PB}. The fuzzy rule set for the compressor plant is
tabulated in Table 1. The E, CE, The Mamdani fuzzy interface system (FIS) are used in the current FLC
design. The fuzzy variables PS, PM, and PB are in the range of [0, 1], ZO is the range of [-0.2 to 0.2], and
NB, NM, and NS are in the range of [-1 to 0] under the universe of discourse. Based on the display range, the
big and small inputs are categorized in rule set inference.
There are 49 rules used for two inputs (E and CE) with 7 fuzzy variables. The fuzzy rules are framed
by analyzing the PD/PID controller performance by tuning the controller constants. The nth
-order rule base
(R) for the given FLC with two inputs (E and CE) is shown in (4):
Rn: IF (error is E(i)) AND (Change in Error is CE(i)) then (FLC output is O(i) (4)
where n is set to 1, 2, …Nmax for the nth
fuzzy rule set. E(i), CE(i), and O(i)=1,2,3,4,5,6,7 are basic fuzzy rule
sets. The 49 rules are used in the rule base, which converts the two inputs to a single output. Finally, the
fuzzification outputs in the ruleset are transformed into the crisp output format using the centroid method in
the de-fuzzification process.
(a) (b)
(c) (d)
Figure 3. Membership functions plots for error, change error (CE), output variables, and surface view of FLC
(a) FLC error input, (b) FLC change in error input, (c) FLC output, and (d) surface view of FLC

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Table 1. The fuzzy rule set for the compressor plant
Output
Error (E)
NB NM NS ZO PS PM PB
Change
in
Error
(CE)
NB NB NB NB NB NM NS ZO
NM NB NB NB NM NS ZO PS
NS NB NB NM NS ZO PS PM
ZO NB NM NS ZO PS PM PB
PS NM NS -ZO PS PM PB PB
PM NS ZO -PS PM PB PB PB
PB ZO PS PM PB PB PB PB
2.3. PI/PD based FLC with gain scheduling approach
Gain scheduling (GS) is a process of monitoring the plant’s operating conditions by simply
changing the controller's parameters. It is mainly used to assist changes in plant gain. The GS provides a
non-linear feedback signal and has a linear controller, which monitors the operating conditions in a
pre-programmed way. Thus, the GS is used to compensate for the known non-linearities and significant
parameter changes of the plant. In general, The GS provides linearization for non-linear processes,
measurement of auxiliary variables, and non-linear transformations. In this work, The PI/PD-based fuzzy
logic controller (FLC) with gain scheduling for a compressor plant is represented in Figure 4. The design
includes Step input, PI/PD-based controller followed by FLC with gain scheduling, non-linear valve, and
process.
Figure 4. Modeling of PI/PD based FLC with gain scheduling (GS)
The FLC controller output is fed through the gain scheduling approach to compensate for
non-linearity before being applied to the non-linear valve and process. The non-linear valve is assumed to
have u2
, and it is greater than zero. The gain scheduling with 2-line segments (a, b) are used to compensate
the non-linearity of the valve and Plant, and it is represented in the (5) and (6):
𝑏 = 0.433𝑎 ; 0 < 𝑎 < 3 (5)
𝑏 = 0.0538𝑎 + 1.158 ;3 < 𝑎 < 10 (6)
The 2-line segments (a and b) are lies in between a=0 and a=10. Therefore, to reduce the non-linearity of the
Plant, increase the line segments in gain scheduling.
3. RESULTS AND DISCUSSION
The PI and PD-based FLC models are designed using a gain scheduling approach for the
compressor system. The work is carried out using the MATLAB Simulink environment. The simulation
results of both PI and PD-based FLC without and with gain scheduling approach are represented. The
Performance parameters for the same are discussed in detail. The PI-FLC model generates the error signal,
and the error integral is represented in Figures 5(a) and 6(b) and Figures 6(a) and 6(b), respectively, for
without and with gain scheduling. Similarly, the error derivative is generated by PD-FLC is represented in
Figures 5(c) and 6(c), respectively, for without and with gain scheduling.
The conventional PD and PID controllers and PI-FLC and PD-FLC’s control output response are
compared without and with gain scheduling represented in Figures 7(a) and 7(b). The step time is set to 1 as
an input to the controller and followed by a process. The output response is controlled by feedback PI-FLC
and PD-FLC controllers and is working based on the operation of the compressor system. The system output
rises at 1.48 sec and 4.26 sec for PI-FLC and PD-FLC without GS, respectively. The PI-FLC output response
settles at 12.24 sec, and similarly, PD-FLC takes a 9.4 sec settling time. Thus, the PD-FLC output response
without GS has less overshoot than the conventional PD and PID controller. The system output rises at

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1.475 sec and 4.19 sec for PI-FLC and PD-FLC with GS, respectively. The PI-FLC output response settles at
10.07 sec, and similarly, for PD-FLC takes a 9.27 sec settling time. The PD-FLC with-GS output response
has less overshoot than the PI-FLC and conventional PD, PID controller with-GS. The PD-FLC with GS
gives better control response output than other controllers.
(a) (b)
(c)
Figure 5. Error signal generation using of FLC controller-without gain scheduling (a) error signal, (b) error
integral, and (c) error derivative
(a) (b)
(c)
Figure 6. Error signal generation using of FLC controller-with gain scheduling, (a) error signal, (b) error
integral, and (c) error derivative

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(a) (b)
Figure 7. Comparison of control output response of different controllers (a) without gain scheduling and
(b) with gain scheduling
The different performance parameters, like rising time (RT), settling time (ST), maximum overshoot
(OS), peak value, and peak time, is calculated based on the control response output of different controllers
without and with GS are tabulated in Table 2. The fuzzy logic Based controllers with GS has less overshoot
than conventional PID controllers with and without GS. On the other hand, the Peak time is relatively high in
the FLC controller than the PID controller because of the process (plant) transfer function. The PI-based FLC
and PD-based FLC with Gain scheduling approach consume less overshoot time of 35.6% and 74%,
respectively, than the PI-based FLC and PD-based FLC without gain scheduling approach.
Table 2. Performance analysis of output response using different controllers
Parameters
Controllers without Gain Scheduling Controllers with Gain Scheduling
PD PID PI-FLC PD-FLC PD PID PI-FLC PD-FLC
Rise Time (RT) 0.6 0.85 1.48 4.26 1.12 1.2 1.475 4.19
Settling Time (ST) 7.01 8.47 12.24 9.4 9.64 7.97 10.07 9.27
% Overshoot (OS) 31.61 42.1 44.36 0.014 61.74 26.6 28.53 0.0036
Peak Time (PT) 3 4 5 19 5 4 5 15
Peak Value 1.18 1.42 1.4 0.97 1.84 1.2 1.27 1
The error analysis regarding simulation time for the control response output using different
controllers with and without gain scheduling is tabulated in Table 3. The error analysis includes integral time
absolute error (ITAE), integral absolute error (IAE), and integral square error (ISE) parameters. The different
controllers with a gain scheduling approach utilize fewer error responses than the different controllers
without a gain scheduling approach. The error response of PD-based FLC with the gain scheduling approach
improves 7.9% in ITAE, 7.4% in IAE, and 16% in ISE than PD-based FLC without gain scheduling
approach.
Table 3. Error analysis of the output response using different controllers
Parameters
Controllers without Gain Scheduling Controllers with Gain Scheduling
PD PID PI-FLC PD-FLC PD PID PI-FLC PD-FLC
ITAE 79.21 3.12 2.935 17.54 10.65 0.338 5.157 16.15
IAE 4.335 1.23 0.147 0.85 0.6972 0.129 0.257 0.787
ISE 0.7671 0.544 6.06E-03 0.018 0.0026 0.0063 0.0018 0.015
4. CONCLUSION AND FUTURE WORK
In this manuscript, The PI/PD-based fuzzy logic controller using the gain scheduling approach is
designed to improve performance constraints for the compressor plant. The PI/PD based fuzzy logic
controller has a compressor plant with a closed-loop system, The PI-based FLC receives P and I constant as
inputs, and similarly, PD-FLC receives P and D constants as an input to the fuzzy system and based on the
Fuzzy rule set, the system output is obtained. The conventional PD/PID controller is also designed for

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comparison purposes. A control output response, controller output, and error signal output are also
represented for all the controllers without gain scheduling. The PI/PD-FLC with GS has less overshoot, better
rise time, settling time, and better error response than the conventional PID controllers with and with GS. In
the future, Performance constraints are still improved by using artificial neural networks (ANN) for
compressor systems.
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BIOGRAPHIES OF AUTHORS
Divya Muddenahalli Narasimhaiah completed her B.E. in Electronics and
Communication Engineering from Bangalore University in 2001, Masters in Electronics
VTU Belgaum in 2007. She is currently pursuing a Ph.D. in Aerospace Electronics. Her
areas of interest are Aerospace Electronics and Signal Processing. She can be contacted at
emai: draophd@gmail.com.
Chikkajala Krishnappa Narayanappa received Ph.D. from Visvesvaraya
Technological University, Belagavi, in 2014 and M. Tech in Biomedical Instrumentation
from Mysore University in 1996. He is currently working as Associate Professor at the
Department of Medical Electronics, Ramaiah Institute of Technology, Bengaluru, since
2000. His research interests include Signal & Image processing and Control Systems. He is
the member of ISTE, IETE and BMESI. He is also a fellow at The Institution of Engineers
(India). He can be contacted at email: c_k_narayanappa@msrit.edu.
Gangadharaiah Soralamavu Lakshmaiah completed B.E. in Electronics and
Communication Engineering from Kuvempu University in 1997, M. Tech in Digital
Electronics and Advanced Communication from KREC, Surathkal in 2002. Currently, he is
pursuing a Ph.D. in the area of VLSI signal processing. His areas of interest are Analog and
Digital VLSI and VLSI Signal Processing. He can be contacted at emai: Gdhar@msrit.edu.

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