A robust optimization strategy for brushless DC motor repetitive controller based on H-infinity
Tianli Wang,
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Tianqing Yuan,
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Jing Bai
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et al.
AIP Advances,
Journal Year:
2024,
Volume and Issue:
14(9)
Published: Sept. 1, 2024
The
control
of
Brushless
DC
(BLDC)
motor
has
many
challenges.
A
large
number
harmonics
are
generated
during
operation,
while
it
is
susceptible
to
external
disturbances
such
as
noise.
torque
ripple
also
a
constraint
the
large-scale
popularity
BLDC
motors
in
some
high-end
industries.
Taking
system
research
object,
this
paper
proposes
method
H-infinity
repetitive
control.
First,
state
space
expression
derived
from
mathematical
model
motor.
Then,
parameters
low-pass
filter
internal
mode
link
obtained
for
design
In
addition,
constructed
and
converted
into
standard
form
system.
Finally,
bringing
generalized
controlled
object
MATLAB
derive
compliant
compensator.
simulation
results
show
that
proposed
can
not
only
suppress
but
improve
robustness
Language: Английский
A rolling bearing fault signal denoising algorithm that combines a new adaptive information entropy with a new wavelet threshold function
Min Li,
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Xuemei Li,
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Bin Liu
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et al.
Engineering Research Express,
Journal Year:
2024,
Volume and Issue:
6(4), P. 045536 - 045536
Published: Oct. 25, 2024
Abstract
Mechanical
fault
diagnosis
is
of
great
significance
to
industrial
automation,
and
extracting
vibration
signals
one
the
important
tasks
in
mechanical
health
monitoring
diagnosis.
However,
due
complex
working
environment
rolling
bearings,
a
large
amount
noise
makes
it
difficult
extract
signals.
Denoising
signal
bearings
can
remove
interference
noise,
simplify
early
identification
features,
thus
improve
diagnostic
accuracy
maintenance
efficiency.
This
paper
proposes
bearing
denoising
algorithm,
which
constructs
new
feature
extraction
function.
method
first
decomposes
noisy
into
Intrinsic
Mode
Functions
(IMFs)
by
Computing
Expressive
Empirical
Decomposition
with
Adaptive
Noise
(ICEEMDAN).
Secondly,
adaptive
information
entropy
threshold
function
constructed
IMFs
from
it.
Then,
IMF
denoised
wavelet
Finally,
noise-free
are
reconstructed
reconstruct
signal.
To
verify
actual
performance
comparative
experiments
were
conducted
on
self-collected
dataset
public
dataset,
results
show
that
this
improves
continuity
reconstruction
various
types
more
effectively
accurately,
thereby
improving
detection
2%–9%.
Language: Английский