Measurement Science and Technology,
Год журнала:
2024,
Номер
35(11), С. 116117 - 116117
Опубликована: Авг. 12, 2024
Abstract
Monorail
cranes
are
crucial
in
facilitating
auxiliary
transportation
within
deep
mining
operations.
As
unmanned
driving
technology
becomes
increasingly
prevalent
monorail
crane
operations,
it
encounters
challenges
such
as
low
accuracy
and
unreliable
attitude
recognition,
significantly
jeopardizing
the
safety
of
Hence,
this
study
proposes
a
dynamic
inclination
estimation
methodology
utilizing
Estimation-Focused-EKFNet
algorithm.
Firstly,
based
on
characteristics
crane,
model
is
established,
which
value
can
be
calculated
real-time
by
extended
Kalman
filter
(EKF)
estimator;
however,
given
complexity
road
conditions,
order
to
improve
recognition
accuracy,
CNN-LSTM-ATT
algorithm
combining
convolutional
neural
network
(CNN),
long
short-term
memory
(LSTM)
attention
mechanism
(ATT)
used
firstly
predict
current
camber
predicted
combined
with
CNN
mechanism,
then
observation
EKF
estimator,
finally
realizes
that
estimator
output
accurate
real-time.
Experimental
results
indicate
that,
compared
unscented
filter,
LSTM-ATT,
CNN-LSTM
algorithms,
enhances
complex
conditions
at
least
52.34%,
improving
reliability.
Its
reaches
99.28%,
effectively
ensuring
for
cranes.
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 Robust and Nonlinear Control,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 1, 2024
ABSTRACT
The
parameter
estimation
problem
for
the
nonlinear
closed‐loop
systems
with
moving
average
noise
is
considered
in
this
article.
For
purpose
of
overcoming
difficulty
that
dynamic
linear
module
and
static
lead
to
identification
complexity
issues,
unknown
parameters
from
both
modules
are
included
a
vector
by
use
key
term
separation
technique.
Furthermore,
an
sliding
window
maximum
likelihood
least
squares
iterative
algorithm
gradient
derived
estimate
parameters.
numerical
simulation
indicates
efficiency
proposed
algorithms.
