Enhanced grid stability and prolonging life span in renewable energy power converters using an advanced Sugeno-type AI-based neuro-fuzzy control
Neural Computing and Applications,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 28, 2025
Язык: Английский
Fuzzy Intervals‐Based Supervisory Control for Nonlinear Cement Grinding Process
Hachem Bennour,
Abderrahim Fayçal Megri
Advanced Control for Applications,
Год журнала:
2025,
Номер
7(2)
Опубликована: Март 18, 2025
ABSTRACT
Controlling
nonlinear
systems
remains
a
complex
challenge,
even
when
their
dynamic
models
are
known,
due
to
inherent
uncertainties
and
unpredictable
behaviors
that
affect
system
performance
stability.
This
complexity
has
led
the
growing
adoption
of
multi‐controller
strategies
supervised
by
advanced
controllers,
offering
substantial
advancements
over
years.
These
have
evolved
from
simple
approaches
sophisticated
techniques
integrate
artificial
intelligence
machine
learning,
significantly
improving
robustness,
performance,
adaptability
control
across
various
industries.
paper
describes
novel
supervisory
approach
for
cement
ball
mill
grinding
system.
The
proposed
combines
two
controllers
under
guidance
fuzzy
supervisor:
A
Proportional‐Integral‐Derivative
(PID)
controller,
fine‐tuned
through
Grey
Wolf
Optimization
(GWO)
algorithm
achieve
rapid
precise
response,
Fuzzy
Logic
Controller
(FLC),
which
delivers
robust
during
steady‐state
operation
while
dealing
with
associated
process.
employs
aggregation
operators,
specifically
2‐additive
Choquet
integral,
arithmetic
mean,
evaluate
tracking
error
its
variation.
evaluations
dynamically
determine
contributions
PID
FLC
ensuring
smooth
transitions
augmenting
benefits
each
controller.
Comparative
analyzes
recent
methods
highlight
superiority
in
achieving
more
stable
efficient
innovative
ensures
flexible
management
studied
system,
enhancing
overall
being
easy
implement.
It
also
provides
better
adaptation
variations
increased
robustness
against
disturbances.
Язык: Английский
Reinforcement learning-enhanced expert mixture of LQR and PID for optimized control in DC–DC boost converters
Electrical Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 14, 2025
Язык: Английский
An Analysis Comparing the Performance of Wind Energy Conversion Systems Utilising FLC Controllers
The Eurasia Proceedings of Science Technology Engineering and Mathematics,
Год журнала:
2024,
Номер
31, С. 11 - 17
Опубликована: Дек. 15, 2024
This
research
gives
a
comparative
analysis
of
Proportional-Integral
(PI)
and
Fuzzy
Logic
Control
(FLC)
controllers
for
control
systems
wind
energy
conversion
(WECS).
The
PI
controller
is
conventional
technique
that
extensively
employed
due
to
its
simplicity
efficacy
in
regulating
system
behaviour
through
the
adjustment
proportional
integral
gains.
FLC,
on
other
hand,
utilises
language
rules
fuzzy
logic
reasoning
imitate
human
decision-making
processes,
providing
flexible
adaptable
strategy.
selection
between
dependent
particular
demands
limitations
application.
comparison
study
evaluates
performance
various
scenarios,
specifically
scenario
speed
cascaded
doubly
fed
induction
generator
(CDFIG).
Wind
consist
CDFIG
connected
grid
via
matrix
converter
or
rectifier
inverter.
will
present
numerical
simulation
results
conducted
using
MATLAB/Simulink
program
demonstrate
feasibility
proposed
Язык: Английский
Comparative study of real-time stator voltage control in stand-alone doubly fed induction generators using PI, fuzzy logic and neural network approaches
STUDIES IN ENGINEERING AND EXACT SCIENCES,
Год журнала:
2024,
Номер
5(3), С. e13001 - e13001
Опубликована: Дек. 31, 2024
This
research
focuses
on
the
experimental
comparative
analysis
of
three
techniques
to
optimize
control
performance
AC
voltage
generated
by
a
doubly
fed
induction
generator
(DFIG)
in
wind
power
generation
system
(WPGS)
serving
local
consumers.
Unlike
grid-connected
operation,
rotor
side
(RSC)
during
stand-alone
operation
maintaining
stable
under
variable
conditions,
such
as
wind-speed
fluctuations
and
load
variations.
Traditional
proportional-integral
(PI)
is
compared
with
intelligent
techniques,
including
fuzzy
logic
artificial
neural
networks
(ANN),
evaluate
their
effectiveness
enhancing
performance.
A
real-time
setup
was
developed
using
3
kW
DFIG
dSPACE
1104
platform
assess
controllers.
The
results
varying
operating
conditions
reveal
that
controllers
significantly
outperform
PI
controller,
particularly
terms
response
time,
robustness,
overshoot.
findings
highlight
potential
enhance
stability
applications.
Язык: Английский