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:
2023,
Номер
34(2), С. 1120 - 1147
Опубликована: Окт. 17, 2023
Abstract
This
article
investigates
the
parameter
identification
problems
of
stochastic
systems
described
by
input‐nonlinear
output‐error
(IN‐OE)
model.
IN‐OE
model
consists
two
submodels,
one
is
an
input
nonlinear
and
other
a
linear
The
difficulty
in
that
information
vector
contains
unknown
variables,
which
are
noise‐free
(true)
outputs
system,
approach
taken
here
to
replace
terms
with
auxiliary
Based
on
over‐parameterization
hierarchical
principle,
gradient‐based
iterative
algorithm
least‐squares‐based
proposed
estimate
parameters
systems.
Finally,
numerical
simulation
examples
given
demonstrate
effectiveness
algorithms.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2023,
Номер
38(1), С. 255 - 278
Опубликована: Окт. 19, 2023
Summary
This
article
proposes
a
novel
identification
framework
for
estimating
the
parameters
of
controlled
autoregressive
moving
average
(CARARMA)
models
with
colored
noise.
By
means
building
an
auxiliary
model
and
using
hierarchical
principle,
this
investigates
highly‐efficient
parameter
estimation
algorithm.
In
order
to
meet
need
identifying
systems
large‐scale
parameters,
whole
CARARMA
system
is
separated
into
two
sets
decomposition
composition
recursive
algorithm
(i.e.,
generalized
extended
least
squares
or
decomposition‐based
algorithm)
presented.
Moreover,
analyzes
convergence
proposed
The
performance
analysis
shows
that
can
reduce
complexity
compared
without
decomposition.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2024,
Номер
38(4), С. 1363 - 1385
Опубликована: Янв. 28, 2024
Summary
By
using
the
collected
batch
data
and
iterative
search,
based
on
filtering
identification
idea,
this
article
investigates
proposes
a
filtered
multi‐innovation
generalized
projection‐based
method,
gradient‐based
least
squares‐based
method
for
equation‐error
autoregressive
systems
described
by
models.
These
methods
can
be
extended
to
other
linear
nonlinear
scalar
multivariable
stochastic
with
colored
noises.
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2023,
Номер
37(8), С. 2247 - 2275
Опубликована: Июнь 6, 2023
Summary
Missing
data
often
occur
in
industrial
processes.
In
order
to
solve
this
problem,
an
auxiliary
model
and
a
particle
filter
are
adopted
estimate
the
missing
outputs,
two
unbiased
parameter
estimation
methods
developed
for
class
of
nonlinear
systems
(e.g.,
bilinear
systems)
with
irregularly
data.
Firstly,
is
constructed
unknown
output,
model‐based
multi‐innovation
recursive
least
squares
algorithm
presented
by
expanding
scalar
innovation
vector.
Secondly,
according
bias
compensation
principle,
proposed
compensate
caused
colored
noise.
Thirdly,
further
improving
accuracy,
true
output
estimated
filter,
filtering‐based
developed.
Finally,
numerical
example
selected
validate
effectiveness
algorithms.
The
simulation
results
indicate
that
algorithms
have
good
performance
identifying
International Journal of Adaptive Control and Signal Processing,
Год журнала:
2023,
Номер
37(11), С. 2983 - 3007
Опубликована: Авг. 23, 2023
Summary
Multiple‐input
multiple‐output
(MIMO)
models
are
widely
used
in
practical
engineering.
This
article
derives
a
new
identification
model
of
the
MIMO
system
by
decomposing
into
several
multiple‐input
single‐output
subsystems.
By
means
auxiliary
idea,
an
model‐based
recursive
least
squares
(AM‐RLS)
algorithm
is
derived
for
identifying
systems.
In
order
to
reduce
computational
burden
systems,
this
presents
hierarchical
applying
principle,
(AM‐HLS)
proposed
improving
efficiency.
The
efficiency
analysis
indicates
that
AM‐HLS
effective
reducing
calculation
amount
compared
with
AM‐RLS
algorithm.
Moreover,
analyzes
convergence
simulation
example
shows
and
algorithms
studied
effective.
