Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering,
Год журнала:
2024,
Номер
238(10), С. 1763 - 1784
Опубликована: Июль 27, 2024
This
paper
mainly
discussed
the
highly
efficient
iterative
identification
methods
for
bilinear
systems
with
autoregressive
moving
average
noise.
Firstly,
input-output
representation
of
is
derived
through
eliminating
unknown
state
variables
in
model.
Then
based
on
maximum-likelihood
principle,
a
gradient-based
(ML-GI)
algorithm
proposed
to
identify
parameters
colored
noises.
For
improving
computational
efficiency,
original
model
divided
into
three
sub-identification
models
smaller
dimensions
and
fewer
parameters,
hierarchical
(H-ML-GI)
by
using
principle.
A
(GI)
given
comparison.
Finally,
algorithms
are
verified
simulation
example
practical
continuous
stirred
tank
reactor
(CSTR)
example.
The
results
show
that
effective
identifying
noises
H-ML-GI
has
higher
efficiency
faster
convergence
rate
than
ML-GI
GI
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 Robust and Nonlinear Control,
Год журнала:
2024,
Номер
34(10), С. 6804 - 6826
Опубликована: Март 21, 2024
Abstract
This
article
considers
the
parameter
estimation
problems
for
controlled
autoregressive
systems
interfered
by
moving
average
noises.
A
recursive
extended
gradient
algorithm
with
penalty
term
is
proposed
using
criterion
function.
By
introducing
three
fictitious
output
variables,
original
system
can
be
decomposed
into
subsystems
based
on
hierarchical
principle.
The
then
to
achieve
estimation.
Finally,
experimental
results
demonstrate
effectiveness
of
algorithms.
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.
