Optimal Control Applications and Methods,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 13, 2025
ABSTRACT
This
article
addresses
the
parameter
estimation
problems
of
bilinear
output‐error
systems,
and
auxiliary
model
identification
idea
particle
filtering
technique
are
adopted
to
overcome
obstacle
resulting
from
unknown
true
outputs.
Then
a
filtering‐based
forgetting
factor
stochastic
gradient
algorithm
is
proposed
for
systems.
To
enhance
convergence
rate
accuracy
estimation,
we
expand
scalar
innovation
an
vector
develop
multi‐innovation
algorithm.
Finally,
numerical
example
practical
continuous
stirred
tank
reactor
process
provided
show
that
discussed
methods
work
well.
The
results
indicate
algorithms
effective
identifying
systems
can
generate
more
accurate
estimates
than
model‐based
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(6), С. 2074 - 2092
Опубликована: Март 27, 2024
Summary
Nonlinear
systems
widely
exist
in
real‐word
applications
and
the
research
for
these
has
enjoyed
a
long
fruitful
history,
including
system
identification
community.
However,
modeling
nonlinear
is
often
quite
challenging
still
remains
many
unresolved
questions.
This
article
considers
online
issue
of
Hammerstein
systems,
whose
static
function
modeled
by
B‐spline
network.
First,
model
studied
constructed
using
bilinear
parameter
decomposition
model.
Second,
recursive
algorithms
are
proposed
to
find
estimates
moving
data
window
particle
swarm
optimization
procedure,
show
that
converge
their
true
values
with
low
computational
burden.
Numerical
examples
also
given
test
effectiveness
algorithms.
International Journal of Robust and Nonlinear Control,
Год журнала:
2024,
Номер
34(11), С. 7265 - 7284
Опубликована: Март 28, 2024
Summary
In
this
paper,
we
use
the
maximum
likelihood
principle
and
negative
gradient
search
to
study
identification
issues
of
multivariate
equation‐error
systems
whose
outputs
are
contaminated
by
an
moving
average
noise
process.
The
model
decomposition
technique
is
used
decompose
system
into
several
regressive
subsystems
based
on
number
outputs.
order
improve
parameter
estimation
accuracy,
a
decomposition‐based
iterative
algorithm
proposed
means
method.
numerical
simulation
example
indicates
that
method
has
better
results
than
compared
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.
