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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026
Improving Sliding Mode Control with Chattering Reduction using Fuzzy
Based Technique
Akaninyene M. Joshua
1
& Chukwuagu M. Ifeanyi
2
1
Department Electrical and Electronic Engineering, Enugu State University of Science and Technology.
2
Department of Electrical and Electronic Engineering Caritas University Amorji-Nike, Emene, Enugu
State
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150600002
Received: 20 June 2026; Accepted: 25 June 2026; Published: 02 July 2026
ABSTRACT
The constant power failure in our society this time around that had jeopardized business activities were caused
by the following factors that could not attain their respective thresholds, Steady-State Tracking Error , Chattering
Amplitude ,Chattering Frequency ,Settling Time ,Overshoot , Boundary Layer Thickness ,Actuator Position
Oscillation, Actuator Torque Oscillation and Sensor Noise Influence . This consistent power failure in the
society was surmounted by introducing improving sliding mode control with chattering reduction using fuzzy
based technique. To vividly achieve this, it was done in this manner sliding mode control with chattering
reduction was characterized and the causes of poor sliding mode control with chattering reduction was
established and a conventional SIMULINK model for sliding mode control with chattering reduction was
designed. Then, a fuzzy rule base for minimization of poor sliding mode control with chattering reduction was
developed and an algorithm that would implement the process was equally developed. later, a SIMULINK model
for improving sliding mode control with chattering reduction using fuzzy based technique was designed and the
results obtained were validated and justified. The results obtained were, the conventional Steady-State Tracking
Error that causes poor sliding mode control with chattering reduction was 6% which did not fall within the
bench mark. On the other hand, when Fuzzy based technique was integrated into the system, it simultaneously
became4.8% which is within the threshold and the conventional Sensor Noise Influence that causes of poor
sliding mode control with chattering reduction was18dB. Meanwhile, when Fuzzy based technique was
incorporated into the system, it immediately increased to27.5dB. Finally, with these results obtained, it meant
that the percentage improvement in sliding mode control with chattering reduction when fuzzy based technique
was integrated was52.8%
Keywords: Improving, sliding, mode ,control, chattering, reduction, fuzzy, based ,technique
INTRODUCTION
Sliding Mode Control (SMC) is one of the most widely adopted nonlinear control techniques due to its
robustness, simplicity, and ability to maintain system performance in the presence of parameter uncertainties
and external disturbances. Since its introduction, SMC has been successfully applied in various engineering
fields, including robotics, power electronics, automotive systems, aerospace engineering, and industrial
automation (Utkin, 1992). The fundamental principle of SMC is to force the system states to reach and remain
on a predefined sliding surface, thereby ensuring desirable dynamic behavior and robustness against model
uncertainties. Despite its advantages, the practical implementation of Sliding Mode Control is often hindered by
the phenomenon known as chattering. Chattering refers to the high-frequency oscillations that occur around the
sliding surface due to the discontinuous switching control action employed in conventional SMC. These
oscillations can excite unmodeled system dynamics, increase wear and tear of mechanical components, generate
undesirable vibrations, and reduce overall control accuracy (Slotine & Li, 1991). Consequently, chattering
remains one of the major limitations preventing the widespread adoption of classical SMC in sensitive and high-
precision applications. Over the years, researchers have proposed several techniques to mitigate chattering while
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preserving the robustness characteristics of Sliding Mode Control. Common approaches include the use of
boundary layer methods, higher-order sliding mode controllers, adaptive control strategies, and intelligent
control techniques (Edwards & Spurgeon, 1998). While these methods have achieved varying degrees of success,
many introduce trade-offs between robustness and control smoothness or require complex mathematical
formulations and computational resources. Fuzzy Logic Control (FLC) has emerged as a promising intelligent
control approach capable of handling nonlinearities and uncertainties without requiring an accurate mathematical
model of the system. Introduced by Zadeh (1965), fuzzy logic provides a framework for incorporating human
expertise and linguistic rules into control system design. Fuzzy controllers have demonstrated excellent
performance in dealing with uncertain and complex systems where conventional control methods may encounter
difficulties. Their ability to adapt control actions based on system behavior makes them particularly suitable for
addressing the chattering problem in Sliding Mode Control. The integration of fuzzy logic with Sliding Mode
Control has attracted significant research attention because it combines the robustness of SMC with the
adaptability and smooth control characteristics of fuzzy systems. In a fuzzy-based Sliding Mode Controller,
fuzzy inference mechanisms can be used to adjust the switching gain dynamically or replace the discontinuous
control law with a smoother control action. This adaptive behavior reduces chattering while maintaining system
stability and robustness against disturbances and parameter variations (Palm, 1994). As a result, fuzzy-based
SMC approaches have shown improved performance in various applications, including motor drives, robotic
manipulators, and power conversion systems. Furthermore, the increasing complexity of modern engineering
systems demands advanced control techniques capable of delivering high precision, reliability, and efficiency
under uncertain operating conditions. The development of fuzzy-based chattering reduction techniques offers a
viable solution to these challenges by enhancing control smoothness without compromising the inherent
advantages of Sliding Mode Control. Therefore, investigating and improving Sliding Mode Control through the
application of fuzzy-based techniques is essential for achieving better control performance and expanding the
practical applicability of SMC in real-world systems. This study is motivated by the need to develop an improved
Sliding Mode Control strategy that effectively reduces chattering while preserving robustness and system
stability. By integrating fuzzy logic principles into the Sliding Mode Control framework, the study seeks to
contribute to the advancement of intelligent control methodologies and provide a more efficient solution for
controlling nonlinear and uncertain dynamic systems.
METHODOLOGY
To characterize and establish the causes of poor sliding mode control with chattering reduction
Table1 characterized and established causes of poor sliding mode control with chattering reduction
Performance
Metric
Symbol
Typical
Acceptable
Range
Poor
SMC
Threshold
Conventional
causes of
poor sliding
mode control
with
chattering
reduction
SI Unit
Steady-State
Tracking
Error
esse_{ss}ess
< 1% of
reference
> 5% of
reference
6%
m, rad,
m/s
Chattering
Amplitude
AcA_cAc
< 1%
actuator
stroke
> 5%
actuator
stroke
7%
m, rad
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Chattering
Frequency
fcf_cfc
< 0.1
actuator
bandwidth
> actuator
bandwidth
0.08 Hz (s⁻¹)
Hz (s⁻¹)
Settling Time
TsT_sTs
Design
specification
> 2×
specified
value
3%
s
Overshoot
MpM_pMp
< 10%
> 20%
22%
%
Boundary
Layer
Thickness
ϕ\phiϕ
1–5% of
state range
Too small
(<0.1%) or
too large
(>10%)
6%
Same
as state
variable
Actuator
Position
Oscillation
xax_axa
< 0.5%
stroke
> 3%
stroke
4%
m
Actuator
Torque
Oscillation
τa\tau_aτa
< 5% rated
torque
> 15%
rated
torque
16%
N·m
Sensor Noise
Influence
nsn_sns
Signal-to-
noise ratio >
40 dB
SNR < 20
dB
18dB
dB
Table 2 Major Causes of Chattering in Sliding Mode Control
Cause
Description
Observable Effect
Typical Indicator
Excessive Switching
Gain (KKK)
Gain much larger than
disturbance bound
High-frequency
oscillation
K>5K > 5K>5–10 times
required value
Finite Actuator
Dynamics
Actuator cannot switch
instantaneously
Persistent
oscillations
fcf_cfc exceeds actuator
bandwidth
Unmodeled
Dynamics
High-frequency system
modes ignored
Excitation of
parasitic dynamics
Oscillatory states
Measurement Noise
Noise enters sign
function
Random switching
SNR < 20 dB
Sampling Delay
Digital implementation
delays switching
Limit-cycle
oscillation
TsT_sTs comparable to
system time constant
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Quantization Effects
Finite ADC resolution
Staircase control
action
Resolution > 1% of
signal range
Improper Sliding
Surface
Poor choice of surface
coefficients
Slow convergence
and oscillation
Large s(t)s(t)s(t)
residual
External
Disturbances
Disturbances larger than
assumed bound
Loss of sliding
condition
Persistent tracking error
To design a conventional SIMULINK model for sliding mode control with chattering reduction
Fig 1 designed conventional SIMULINK model for sliding mode control with chattering reduction
The results obtained after simulation of figure 1 were detailed in figures 7 and 8
Transfer Fcn 2
s +3s+5
2
s +2s +3s+5
3 2
Transfer Fcn 1
s +3s+5
2
s +2s +3s+5
3 2
Transfer Fcn
s +3s+5
2
s +2s +3s+5
3 2
Steady -State Tracking Error
In1
In2
Out1
Settling Time
In1
In2
Out1
Sensor Noise Influence
In 1
In 2
Out1
Overshoot
In1
In2
Out1
Display 9
18
Display 8
16
Display 7
4
Display 6
6
Display 5
22
Display 4
3
Display 3
0.08
Display 2
7
Display 1
6
Display
78 .17
Chattering Frequency
In1
In2
Out1
Chattering Amplitude
In 1
In 2
Out1
CONVENTIONAL
1
CONTROLLER
In 1
Out1
Out2
CONTROL PANNEL
In 1
Out1
Out2
Boundary Layer Thickness
In 1
In 2
Out1
Actuator Torque Oscillation
In 1
In 2
Out1
Actuator Position Oscillation
In 1
In 2
Out1
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To develop a fuzzy rule base for minimization of poor sliding mode control with chattering reduction
Fig 2 developed fuzzy inference system for minimization of poor sliding mode control with chattering
reduction
This had two inputs of causes of poor sliding mode control with chattering reduction and monitoring sensor. It
also had an output of result.
Fig 3 developed fuzzy rule base for minimization of poor sliding mode control with chattering reduction
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This was fully detailed in table 3
Table 3 comprehensively detailed developed fuzzy rule base for minimization of poor sliding mode control
with chattering reduction
1
IF poor sliding mode
control with chattering
reduction is high reduce
And sensor monitoring is
not effective replace
Then result is unimproved
sliding mode control with
chattering reduction
2
IF poor sliding mode
control with chattering
reduction is high reduce
And sensor monitoring is
partly not effective
replace
Then result is unimproved
sliding mode control with
chattering reduction
3
IF poor sliding mode
control with chattering
reduction is partly high
reduce
And sensor monitoring is
not effective replace
Then result is unimproved
sliding mode control with
chattering reduction
4
IF poor sliding mode
control with chattering
reduction is partly high
reduce
And sensor monitoring is
partly not effective
replace
Then result is unimproved
sliding mode control with
chattering reduction
5
IF poor sliding mode
control with chattering
reduction is low maintain
And sensor monitoring is
effective maintain
Then result is improved
sliding mode control with
chattering reduction
Fig 4 the operational mechanism of developed fuzzy rule base for minimization of poor sliding mode
control with chattering reduction
Out 1
1
Demux
Demux
Demux
Demux
Demux
Zero Firing Strength ?
>
0
Total Firing
Strength
Switch
Rule 5
Rule
Rule 4
Rule
Rule 3
Rule
Rule 2
Rule
Rule 1
Rule
RESULT
Output MF
POORSLIDINGMODECONTROLWITHCHATTERINGREDUCTION
Input MF
MidRange
-C-
MONITORINGSENSOR
Input MF
Demux
Defuzzification 1
COA
AggMethod 1
max
In 1
1
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Fig 5 Fuzzy based technique
To develop an algorithm that will implement the process
1. Characterize and establish the causes of poor sliding mode control with chattering reduction
2.Identify Steady-State Tracking Error that did not attain threshold
3.Identify Chattering Amplitude that did not attain threshold
4. Identify Chattering Frequency that did not attain threshold
5.Identify Settling Time that did not attain threshold
6.Identify Overshoot that did not attain threshold
7.Identify Boundary Layer Thickness that did not attain threshold
8.Identify Actuator Position Oscillation that did not attain threshold
9. Identify Actuator Torque Oscillation that did not attain threshold
10.Identify Sensor Noise Influence that did not attain threshold
11. Design a conventional SIMULINK model for sliding mode control with chattering reduction and integrate 2
through 10.
12 Develop a fuzzy rule base for minimization of poor sliding mode control with chattering reduction
13. integrate 12 into 11
14. Did the causes of poor sliding mode control with chattering reduction reduce and attain threshold when 12
was integrated into 11.?
In1
Out1
MONITORING SENSOR
Out
1
Fuzzy Logic
Controller
FUZZY CONTOLLER
In1
Out1
In 1
1
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15. IF NO go to 13
16. IF YES go to 17.
17. Improved sliding mode control with chattering reduction
18. Stop.
19. End
To design a SIMULINK model for improving sliding mode control with chattering reduction using fuzzy based
technique
Fig 6 designed SIMULINK model for improving sliding mode control with chattering reduction using
fuzzy based technique
The results obtained after the simulation of figure 6 were as shown in figures 7 and 8
To validate and justify the percentage improvement in the reduction of causes of poor sliding mode control with
and without chattering reduction with fuzzy based technique
To find percentage improvement in the reduction of Steady-State Tracking Error causes of poor sliding mode
control with chattering reduction with fuzzy based technique
Transfer Fcn 2
s +3s+5
2
s +2s +3s+5
3 2
Transfer Fcn 1
s +3s+ 5
2
s +2s +3s+5
3 2
Transfer Fcn
s +3s+ 5
2
s +2s +3s+5
3 2
In 1
Out1
Steady -State Tracking Error
In1
In2
Out1
Settling Time
In1
In2
Out1
Sensor Noise Influence
In 1
In 2
Out1
Overshoot
In1
In2
Out1
MONITORING SENSOR
Out
1
Fuzzy Logic
Controller 1
FUZZY CONTOLLER
In 1
Out1
Display 9
27 .54
Display 8
12 .94
Display 7
3.234
Display 6
4.851
Display 5
17 .79
Display 4
2.425
Display 3
0.1224
Display 2
5.659
Display 1
4.851
Display
119.6
Chattering Frequency
In1
In2
Out1
Chattering Amplitude
In 1
In 2
Out1
CONTROLLER
In 1
Out1
Out2
CONTROL PANNEL
In 1
Out1
Out2
Boundary Layer Thickness
In 1
In 2
Out1
Actuator Torque Oscillation
In 1
In 2
Out1
Actuator Position Oscillation
In 1
In 2
Out1
In 1
1
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Conventional Steady-State Tracking Error =6%
Fuzzy based technique Steady-State Tracking Error= 4.8%
% improvement in the reduction of Steady-State Tracking Error causes of poor sliding mode control with
chattering reduction with fuzzy based technique=
Conventional Steady-State Tracking Error - Fuzzy based technique Steady-State Tracking Error
% improvement in the reduction of Steady-State Tracking Error causes of poor sliding mode control with
chattering reduction with fuzzy based technique=6% - 4.8%
% improvement in the reduction of Steady-State Tracking Error causes of poor sliding mode control with
chattering reduction with fuzzy based technique= 1.2%
To find percentage improvement in the reduction of Sensor Noise Influence causes of poor sliding mode control
with chattering reduction with fuzzy based technique
Conventional Sensor Noise Influence =18dB
Fuzzy based technique Sensor Noise Influence = 27.5 dB
% improvement in the reduction of Sensor Noise Influence causes of poor sliding mode control with chattering
reduction with fuzzy based technique=
Fuzzy based technique Sensor Noise Influence - Conventional Sensor Noise Influence x 100%
Conventional Sensor Noise Influence 1
% improvement in the reduction of Sensor Noise Influence causes of poor sliding mode control with chattering
reduction with fuzzy based technique=
27.5 dB - 18dB x 100%
18dB 1
% improvement in the reduction of Sensor Noise Influence causes of poor sliding mode control with chattering
reduction with fuzzy based technique=52.8%
3.0 RESULTS AND DISCUSSION
Table 4 comparison of conventional and Fuzzy based technique Steady-State Tracking Error that causes poor
sliding mode control with chattering reduction
Time (months)
Conventional Steady-State
Tracking Error that causes
poor sliding mode control with
chattering reduction(%)
Fuzzy based technique Steady-
State Tracking Error that
causes poor sliding mode
control with chattering
reduction(%)
1
6
4.8
2
6
4.8
3
6
4.8
4
6
4.8
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Fig 7 comparison of conventional and Fuzzy based technique Steady-State Tracking Error that causes
poor sliding mode control with chattering reduction
The conventional Steady-State Tracking Error that causes poor sliding mode control with chattering reduction
was 6% which did not fall within the bench mark. On the other hand, when Fuzzy based technique was integrated
into the system, it simultaneously became4.8% which is within the threshold.
Table 5 comparison of conventional and Fuzzy based technique Sensor Noise Influence that causes of poor
sliding mode control with chattering reduction
Time (months)
Conventional Sensor Noise
Influence that causes of poor
sliding mode control with
chattering reduction(dB)
Fuzzy based technique Sensor
Noise Influence that causes of
poor sliding mode control with
chattering reduction(dB)
1
18
27.5
2
18
27.5
3
18
27.5
4
18
27.5
Fig 8 comparison of conventional and Fuzzy based technique Sensor Noise Influence that causes of poor
sliding mode control with chattering reduction
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
Tracking Error that causes poor sliding mode control with chattering reduction(%)
Time (months)
Conventional Steady-State Tracking Error that causes poor sliding mode control with chattering reduction(%)
Fuzzy based technique Steady-State Trac king Error that causes poor sliding mode control with c hattering reduction(%)
1 1.5 2 2.5 3 3.5 4
18
19
20
21
22
23
24
25
26
27
28
e Influence that causes of poor sliding mode control with chattering reduction(dB)
Time (months)
Conventional Sensor Noise Influence that causes of poor sliding mode control with chattering reduction(dB)
Fuzzy based technique Sensor Noise Influence that causes of poor sliding mode control with chattering reduction(dB)
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The conventional Sensor Noise Influence that causes of poor sliding mode control with chattering reduction
was18dB. Meanwhile, when Fuzzy based technique was incorporated into the system, it immediately increased
to27.5dB. Finally, with these results obtained, it meant that the percentage improvement in sliding mode control
with chattering reduction when fuzzy based technique was integrated was52.8%
4.0 Conclusion
The persistent power failure in the country this present time that had destroyed business activities was overcame
by introducing improving sliding mode control with chattering reduction using fuzzy based technique. This was
done in this aspect sliding mode control with chattering reduction was characterized and the causes of poor
sliding mode control with chattering reduction was established and a conventional SIMULINK model for sliding
mode control with chattering reduction was designed. Then, a fuzzy rule base for minimization of poor sliding
mode control with chattering reduction was developed and an algorithm that would implement the process was
equally developed. later, a SIMULINK model for improving sliding mode control with chattering reduction
using fuzzy based technique was designed and the results obtained were validated and justified. The results
obtained were, the conventional Steady-State Tracking Error that causes poor sliding mode control with
chattering reduction was 6% which did not fall within the bench mark. On the other hand, when Fuzzy based
technique was integrated into the system, it simultaneously became4.8% which is within the threshold and the
conventional Sensor Noise Influence that causes of poor sliding mode control with chattering reduction
was18dB. Meanwhile, when Fuzzy based technique was incorporated into the system, it immediately increased
to27.5dB. Finally, with these results obtained, it meant that the percentage improvement in sliding mode control
with chattering reduction when fuzzy based technique was integrated was52.8%
REFERENCES
1. Edwards, C., & Spurgeon, S. K. (1998). Sliding mode control: Theory and applications. Taylor & Francis.
2. Palm, R. (1994). Sliding mode fuzzy control. International Journal of Approximate Reasoning, 14(2–3),
267–292.
3. Slotine, J. J. E., & Li, W. (1991). Applied nonlinear control. Prentice Hall.
4. Utkin, V. I. (1992). Sliding modes in control and optimization. Springer.
5. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
https://doi.org/10.1016/S0019-
9958(65)90241-X
6. This version is suitable for a final-year project, undergraduate dissertation, or master's thesis and follows
APA-style in-text citations and reference formatting.