International Journal of Adaptive Control and Signal Processing,
Journal Year:
2024,
Volume and Issue:
38(9), P. 3213 - 3232
Published: July 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 Adaptive Control and Signal Processing,
Journal Year:
2023,
Volume and Issue:
38(2), P. 513 - 533
Published: Nov. 15, 2023
Summary
In
order
to
solve
the
problem
of
parameter
identification
for
large‐scale
multivariable
systems,
which
leads
a
large
amount
computation
algorithms,
two
recursive
least
squares
algorithms
are
derived
according
characteristics
systems.
To
further
reduce
and
cut
down
redundant
estimation,
we
propose
coupled
algorithm
based
on
coupling
concept.
By
same
estimates
between
sub‐identification
estimation
subsystem
vectors
avoided.
Compared
with
proposed
in
this
article
have
higher
computational
efficiency
smaller
errors.
Finally,
simulation
example
tests
effectiveness
algorithm.
International Journal of Adaptive Control and Signal Processing,
Journal Year:
2024,
Volume and Issue:
38(9), P. 3213 - 3232
Published: July 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.
