Energies,
Год журнала:
2024,
Номер
17(9), С. 2145 - 2145
Опубликована: Апрель 30, 2024
This
paper
considers
the
estimation
of
SOC
and
SOH
for
lithium
batteries
using
multi-innovation
Levenberg–Marquardt
adaptive
weighting
unscented
Kalman
filter
algorithms.
For
parameter
identification,
second-order
derivative
objective
function
to
optimize
traditional
gradient
descent
algorithm
is
used.
estimation,
an
proposed
deal
with
nonlinear
update
problem
mean
covariance,
which
can
substantially
improve
accuracy
internal
state
battery.
Compared
fixed
weights
in
filtering
algorithm,
this
adaptively
adjusts
according
measured
values
accuracy.
Finally,
simulations,
errors
are
all
lower
than
1.63
%,
confirms
effectiveness
algorithm.
Optimal Control Applications and Methods,
Год журнала:
2024,
Номер
45(5), С. 2346 - 2363
Опубликована: Июнь 17, 2024
Abstract
This
article
considers
the
iterative
identification
problems
for
a
class
of
feedback
nonlinear
systems
with
moving
average
noise.
The
model
contains
both
dynamic
linear
module
and
static
module,
which
brings
challenges
to
identification.
By
utilizing
key
term
separation
technique,
unknown
parameters
from
modules
are
included
in
parameter
vector.
Furthermore,
an
auxiliary
maximum
likelihood
gradient‐based
algorithm
is
derived
estimate
parameters.
In
addition,
stochastic
gradient
as
comparison.
numerical
simulation
results
indicate
that
can
effectively
get
more
accurate
estimates
than
algorithm.
Optimal Control Applications and Methods,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 29, 2025
ABSTRACT
Through
dividing
a
multivariable
system
into
several
subsystems,
this
paper
derives
the
sub‐identification
model.
Utilizing
obtained
model,
an
auxiliary
model‐based
maximum
likelihood
forgetting
gradient
algorithm
is
derived.
Considering
enhancing
parameter
estimation
accuracy,
multi‐innovation
(AM‐ML‐MIFG)
proposed
taking
advantage
of
identification
theory.
Simulation
results
test
effectiveness
algorithms,
and
confirm
that
AM‐ML‐MIFG
has
satisfactory
performance
in
capturing
dynamic
properties
system.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(9), С. 3134 - 3160
Опубликована: Июнь 28, 2024
Summary
In
practical
applications,
many
processes
have
nonlinear
characteristics
that
require
models
for
accurate
description.
However,
constructing
such
and
determining
their
parameters
are
a
challenging
task.
This
article
explores
filtered
identification
methods
estimating
the
of
particular
type
Hammerstein
systems
with
ARMA
noise.
An
auxiliary
model‐based
least
squares
algorithm
is
developed
based
on
model
idea.
A
hierarchical
utilizes
principle
proposed
to
enhance
computational
efficiency.
Additionally,
key
term
separation
technique
employed
express
system
output
as
linear
combination
parameters,
allowing
be
decomposed
into
smaller
subsystems
more
efficient
estimation
parameters.
Simulation
results
demonstrate
effectiveness
these
algorithms.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(9), С. 3213 - 3232
Опубликована: Июль 3, 2024
Summary
In
industrial
process
control
systems,
parameter
estimation
is
crucial
for
controller
design
and
model
analysis.
This
article
examines
the
issue
of
identifying
parameters
in
continuous‐time
models.
presents
a
stochastic
gradient
algorithm
recursive
least
squares
continuous
systems.
It
derives
identification
linear
systems
based
on
Laplace
transforms
input
output
To
prove
that
techniques
given
here
work,
we
have
included
simulated
example.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(10), С. 3268 - 3289
Опубликована: Июль 29, 2024
Summary
This
paper
deals
with
the
problem
of
parameter
estimation
for
feedback
nonlinear
output‐error
systems.
The
auxiliary
model‐based
recursive
least
squares
algorithm
and
stochastic
gradient
are
derived
estimation.
Based
on
process
theory,
convergence
proposed
algorithms
proved.
simulation
results
indicate
that
can
estimate
parameters
systems
effectively.
