Auxiliary Model‐Based Maximum Likelihood Multi‐Innovation Forgetting Gradient Identification for a Class of Multivariable Systems
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.
Язык: Английский
A novel filtering-based recursive identification method for a fractional-order Hammerstein state space system with piecewise nonlinearity
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 28, 2025
This
paper
investigates
parameters
and
states
estimation
for
a
class
of
fractional-order
state
space
systems
with
colored
noises.
To
provide
accurate
parameter
estimation,
we
suggest
novel
gradient
descent
algorithm
based
on
the
extended
Kalman
filtering.
The
new
approach
features
lower
error
variances
faster
convergence
rate
than
conventional
algorithm.
A
data
filtering
is
introduced
to
filter
input
output
data,
thereby
reducing
impact
noises
accuracy
estimates.
Язык: Английский
Robust recursive estimation for the errors-in-variables nonlinear systems with impulsive noise
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 19, 2025
The
non-Gaussian
characteristic
of
the
external
disturbance
poses
a
great
challenge
for
system
modeling
and
identification.
This
paper
develops
robust
recursive
estimation
algorithm
errors-in-variables
nonlinear
with
impulsive
noise.
is
formulated
by
minimizing
continuous
logarithmic
mixed
p-norm
criterion,
capable
giving
against
noise
through
an
adjustable
weight
gain.
monomials
noisy
input
are
estimated
expressions
based
on
bias
correction.
Furthermore,
hierarchical
derived
to
reduce
computational
loads.
simulation
studies
demonstrate
feasibility
proposed
algorithms.
Язык: Английский
Iterative parameter estimation for a class of fractional-order Hammerstein nonlinear systems disturbed by colored noise
Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 28, 2025
Considering
the
existence
of
nonlinearity
and
fractional-order
phenomena
in
practical
environments,
this
paper
investigates
parameter
estimation
methods
for
a
class
Hammerstein
nonlinear
systems
disturbed
by
colored
noise.
The
are
based
on
decomposition
strategy,
separating
identification
from
system
parameters.
Meanwhile,
is
divided
into
two
subsystems,
which
linear
block
using
hierarchical
principle.
To
overcome
problem
redundant
estimation,
over-parameterization
method
key
item
separation
used,
respectively.
Then,
two-stage
gradient-based
iterative
algorithm
term
derived,
auxiliary
model
used
to
compute
unmeasurable
variables.
In
addition,
we
analyze
computational
efficiencies
proposed
algorithms.
simulation
results
indicate
that
algorithms
effective.
Finally,
evaluated
through
battery
model.
show
well
agreement
with
real
outputs.
Язык: Английский
Hierarchical Least Squares Identification for the Multivariate Input Nonlinear Controlled Autoregressive Moving Average Systems
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 28, 2025
ABSTRACT
This
article
presents
a
decomposition‐based
least
squares
estimation
algorithm
for
the
multivariate
input
nonlinear
system.
By
using
hierarchical
identification
principle,
breaks
down
system
into
two
subsystems,
one
containing
parameters
of
linear
dynamic
block
and
other
static
block.
Treating
unknown
variables
contained
in
information
vector
model
is
to
replace
them
with
outputs
an
auxiliary
model.
The
comparative
results
between
recursive
developed
this
are
provided
test
proposed
algorithms
have
lower
computational
cost
higher
accuracy.
Furthermore,
convergence
analyzed,
which
can
guarantee
stability
algorithm.
simulation
confirm
efficacy
derived
effectively
estimating
systems.
Язык: Английский
Identification for Precision Mechatronics: An Auxiliary Model‐Based Hierarchical Refined Instrumental Variable Algorithm
International Journal of Robust and Nonlinear Control,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 4, 2025
ABSTRACT
When
the
physical
properties
of
mechanical
systems
align
with
structure
model,
continuous‐time
(CT)
can
be
effectively
represented
by
an
interpretable
and
parsimonious
additive
formal
models.
This
article
addresses
parameter
estimation
challenges
CT
autoregressive
moving
average
(ACTARMA)
systems.
Based
on
maximum
likelihood
principle,
optimality
conditions
for
proposed
identification
algorithms
are
formulated
ACTARMA
Additionally,
auxiliary
model‐based
hierarchical
refined
instrumental
variable
(AM‐HRIV)
iterative
algorithm
AM‐HRIV
recursive
developed
means
principle
model
idea.
These
establish
a
pseudo‐linear
regression
relationship
involving
optimal
prefilters
derived
from
unified
model.
The
effectiveness
methods
is
demonstrated
numerical
simulation,
performance
method
in
identifying
modal
representations
verified
experimental
data.
Язык: Английский
The Aitken Accelerated Gradient Algorithm for a Class of Dual‐Rate Volterra Nonlinear Systems Utilizing the Self‐Organizing Map Technique
International Journal of Robust and Nonlinear Control,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 22, 2025
ABSTRACT
This
article
focuses
on
the
parameter
estimation
issues
for
dual‐rate
Volterra
fractional‐order
autoregressive
moving
average
models.
In
case
of
sampling,
we
derive
a
identification
model
system
and
implement
intersample
output
with
help
an
auxiliary
method.
Then,
combined
self‐organizing
map
technique,
propose
Aitken
multi‐innovation
gradient‐based
iterative
algorithm.
The
parameters
are
estimated
using
algorithm,
whereas
differential
orders
determined
Moreover,
computational
cost
proposed
algorithm
is
analyzed
floating
point
operation.
Finally,
convergence
analysis
simulation
examples
show
effectiveness
Язык: Английский
A Novel Filtering Based Maximum Likelihood Generalized Extended Gradient Method for Multivariable Nonlinear Systems
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 25, 2024
ABSTRACT
This
study
proposes
a
filtering
based
maximum
likelihood
generalized
extended
gradient
algorithm
for
multivariable
nonlinear
systems
with
autoregressive
moving
average
noises.
The
parameter
estimates
are
obtained
by
minimizing
the
half
squared
residual
measurement
which
can
approach
true
values.
An
auxiliary
model
is
also
established
measurable
information
of
system,
and
output
used
to
replace
unmeasurable
variables
so
that
approximates
these
variables,
as
obtain
consistent
estimation
system
parameters.
A
derived
comparison
numerical
example
provided
show
effectiveness
proposed
method
converge
actual
values
quickly.
Язык: Английский