Mathematics,
Journal Year:
2024,
Volume and Issue:
12(22), P. 3504 - 3504
Published: Nov. 9, 2024
In
this
paper,
a
robust
bumpless
transfer
control
scheme
for
tracking
is
proposed
to
avoid
large
jumps
in
the
signals
switched
system
(SS)
with
unmatched
uncertainty
and
disturbance.
The
controller
comprises
linear
feedback
(RLFC)
continuous
sliding
mode
(CSMC)
based
on
given
integral
(RISM).
RLFC
meets
requirement
of
indices,
CSMC
suppresses
First,
design
proposed,
coefficients
satisfy
despite
Then,
RISM
surface
which
uncertain
SS
satisfies
H-infinity
performance
index,
can
resist
uncertainty.
Consequently,
ensures
that
be
reached
finite
time
from
initial
instant.
By
composing
RLFC,
achieves
trajectory
suppression
signal
bumps
during
switching.
Finally,
was
applied
different
examples,
simulation
results
verified
its
effectiveness.
Asian Journal of Control,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 21, 2025
Summary
This
paper
addresses
the
problem
of
a
hierarchical
sliding
mode
surface
(HSMS)
control
design
for
nonlinear
systems
via
dynamic
event‐triggered
mechanism.
Initially,
HSMS
containing
system
states
is
constructed
to
enhance
system's
response
rate
and
robustness.
By
assigning
cost
function
associated
with
HSMS,
such
an
equivalently
transformed
into
zero‐sum
game
problem,
where
policy
exogenous
disturbance
are
treated
as
two
players
opposite
interests.
Afterwards,
novel
mechanism
designed,
triggering
condition
depends
on
variables.
To
solve
corresponding
Hamilton–Jacobi–Isaacs
equation,
single‐critic
reinforcement
learning
algorithm
developed,
which
removes
error
generated
by
approximating
actor
network
in
actor‐critic
network.
According
Lyapunov
stability
theory,
all
signals
considered
strictly
proved
be
bounded.
Finally,
validity
proposed
method
demonstrated
through
simulations
tunnel
diode
circuit
mass‐spring‐damper
system.
Sensors,
Journal Year:
2025,
Volume and Issue:
25(5), P. 1527 - 1527
Published: Feb. 28, 2025
The
article
presents
a
detailed
exposition
of
hardware–software
complex
that
has
been
developed
for
the
purpose
enhancing
productivity
accounting
state
production
process.
This
facilitates
automation
identification
parts
in
containers
and
utilisation
supplementary
markers.
comprises
mini
computer
(system
unit
industrial
version)
with
connected
cameras
(IP
or
WEB),
communication
module
LED
signal
lamps,
software.
cascade
algorithm
detection
labels
objects
employs
trained
convolutional
neural
networks
(YOLO
VGG19),
thereby
recognition
accuracy
while
concurrently
reducing
size
training
sample
networks.
efficacy
system
was
assessed
through
laboratory
experimentation,
which
yielded
experimental
results
demonstrating
93%
detail
using
algorithm,
comparison
to
72%
achieved
traditional
approach
employing
single
network.
