Optimal Control Applications and Methods,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 19, 2025
ABSTRACT
This
study
introduces
a
novel
master–slave
architecture
featuring
an
improved
gradient‐based
optimizer
(ImGBO)
to
effectively
tune
cascaded
proportional‐integral
(PI)
and
proportional‐derivative
with
filter
(PDN)
controller
specifically
for
DC
motor
speed
regulation.
The
core
novelty
of
this
work
lies
in
enhancing
the
traditional
GBO
algorithm
by
integrating
experience‐based
perturbed
learning
mechanism
adaptive
local
search
strategy,
significantly
its
ability
balance
exploration
exploitation
during
optimization.
proposed
ImGBO‐based
PI‐PDN
is
comprehensively
evaluated
against
GBO,
recent
metaheuristics
advanced
proportional‐integral‐derivative
(PID)
fractional‐order
PID
(FOPID)
controllers.
Significant
improvements
were
observed,
method
demonstrating
exceptionally
short
rise
(0.0089
s)
settling
times
(0.0140
s),
no
overshoot,
minimal
steady‐state
error
(0.0017%).
Stability
analysis
via
pole
placement
Bode
plots
affirmed
robust
stable
operation
controller,
exhibiting
phase
margin
71.6640°
infinite
gain
margin.
These
results
strongly
support
suitability
effectiveness
approach
precision‐critical
control
applications.
IET Renewable Power Generation,
Год журнала:
2024,
Номер
18(6), С. 959 - 978
Опубликована: Фев. 20, 2024
Abstract
The
pressing
need
for
sustainable
energy
solutions
has
driven
significant
research
in
optimizing
solar
photovoltaic
(PV)
systems
which
is
crucial
maximizing
conversion
efficiency.
Here,
a
novel
hybrid
gazelle‐Nelder–Mead
(GOANM)
algorithm
proposed
and
evaluated.
GOANM
synergistically
integrates
the
gazelle
optimization
(GOA)
with
Nelder–Mead
(NM)
algorithm,
offering
an
efficient
powerful
approach
parameter
extraction
PV
models.
This
investigation
involves
thorough
assessment
of
algorithm's
performance
across
diverse
benchmark
functions,
including
unimodal,
multimodal,
fixed‐dimensional
CEC2020
functions.
Notably,
consistently
outperforms
other
approaches,
demonstrating
enhanced
convergence
speed,
accuracy,
reliability.
Furthermore,
application
extended
to
single
diode
double
models
RTC
France
cell
model
Photowatt‐PWP201
module.
experimental
results
demonstrate
that
approaches
terms
accurate
estimation,
low
root
mean
square
values,
fast
convergence,
alignment
data.
These
emphasize
its
role
achieving
superior
efficiency
renewable
systems.
e-Prime - Advances in Electrical Engineering Electronics and Energy,
Год журнала:
2023,
Номер
5, С. 100225 - 100225
Опубликована: Июль 29, 2023
In
the
field
of
digital
filter
design
and
system
identification,
accurately
modeling
Infinite
Impulse
Response
(IIR)
systems
is
utmost
importance.
This
paper
introduces
a
new
adaptive
algorithm
that
combines
gazelle
optimization
with
simulated
annealing
to
achieve
superior
performance
for
IIR
filters
used
in
identification.
To
evaluate
algorithm's
effectiveness,
extensive
experiments
were
conducted
on
two
fronts:
challenging
CEC2020
benchmark
functions
fifth
order
identification
problem.
The
proposed
proves
its
effectiveness
by
achieving
optimal
solutions
when
compared
other
well-known
algorithms
such
as
arithmetic
optimization,
particle
swarm
differential
evolution,
sine
cosine
algorithm,
grey
wolf
optimizer,
biogeography-based
salp
original
algorithm.
measured
ability
find
best
these
functions.
For
problem,
study
considers
both
same
reduced
cases.
be
identified
represented
plant.
statistical
analysis
convergence
behavior
are
artificial
hummingbird
mountain
widely
bee
colony
These
comparisons
demonstrate
capability
efficiency
identifying
parameters
systems.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 105830 - 105842
Опубликована: Янв. 1, 2023
This
paper
addresses
the
design
of
an
optimally
executed
proportional-integral-derivative
(PID)
controller,
tailored
for
speed
regulation
a
direct
current
(DC)
motor.
To
achieve
this
objective,
we
present
novel
hybrid
algorithm,
combining
gazelle
optimization
algorithm
(GOA)
with
effective
simplex
search
method
known
as
Nelder-Mead
(NM)
technique.
The
fusion
these
algorithms
yields
innovative
hybridized
version,
striking
balance
between
exploration
and
exploitation.
proposed
approach,
named
optimizer
(GSO),
showcases
great
promise
when
applied
to
task
controlling
DC
motor
using
PID
controller.
identify
optimal
values
gains,
harness
power
objective
function
well,
which
guides
GSO
in
determining
most
favorable
controller
settings.
Rigorous
comparative
simulations
are
then
undertaken,
where
pit
against
several
other
algorithms,
namely
reptile
prairie
dog
weighted
mean
vectors
optimization,
original
GOA
algorithm.
These
allow
us
assess
system's
behavior
through
various
lenses,
such
statistical
tests,
time
frequency
domain
responses,
robustness
analysis,
changes
function.
evaluations
from
comprehensive
tests
demonstrate
superiority
GSO-based
controlled
system.
exhibits
better
performance
than
alternative
providing
solid
evidence
its
effectiveness.
Furthermore,
approach
outperforms
reported
tuning
methods,
affirming
prowess
achieving
superior
motors.
