Actuators,
Journal Year:
2023,
Volume and Issue:
12(11), P. 399 - 399
Published: Oct. 25, 2023
For
an
engraving
machine
system
with
input
dynamic
disturbance
and
output
random
measurement
noise,
a
two-degrees-of-freedom
proportional
integral
derivative
(2-DOF
PID)
control
method
based
on
the
Kalman
filter
is
firstly
proposed
in
this
paper,
which
can
effectively
reject
ensure
set
point
tracking
performance
of
controller.
The
2-DOF
controller
consists
rejection
composed
PID
observer
expectation
model.
parameters
are
tuned
using
differential
evolution
algorithm
(DE),
cumulative
absolute
error
value
(IAE)
used
as
fitness
function
DE
algorithm,
improve
rationality
intelligent
parameter
tuning.
In
addition,
also
applied
to
deal
noise
suppress
influence
uncertainty.
Finally,
compared
existing
algorithms,
feasibility
superiority
verified
numerical
simulation
experimental
test.
Mechanical sciences,
Journal Year:
2025,
Volume and Issue:
16(1), P. 209 - 225
Published: April 16, 2025
Abstract.
Bolt
connections
are
common
in
industrial
and
manufacturing
applications;
however,
improper
torque
tightening
can
lead
to
issues
such
as
over-tightening
or
under-tightening,
which
negatively
affect
connection
quality
lifespan.
To
enhance
the
precision
of
bolt
tightening,
this
paper
introduces
a
particle
swarm
optimization
(PSO)
algorithm
optimize
fuzzy
proportional–integral–derivative
(FPID)
controller.
Effective
methods
for
adjusting
parameters
PSO
FPID
control
systems
also
explored
improve
performance
while
ensuring
stability
reliability
under
complex
load
conditions.
Simulations
were
conducted
using
MATLAB
Simulink
compare
speeds
PSO-optimized
controller,
PID
traditional
Results
indicate
that
controller
significantly
improves
response
speed,
reduces
overshoot,
enhances
system's
adaptability
robustness.
In
experiments
targeting
12
N
m,
average
deviation
is
0.108
achieving
accuracy
0.9
%.
These
findings
validate
effectiveness
proposed
system
demonstrate
marked
improvement
tightening.
Overall,
research
highlights
potential
integrating
into
reliability,
addressing
critical
aspect
fastening.
Automation,
Journal Year:
2025,
Volume and Issue:
6(2), P. 18 - 18
Published: April 27, 2025
Given
the
growing
need
to
enhance
accuracy
of
exploration
robots,
this
study
focuses
on
designing
a
teleoperated
navigation
system
for
robot
equipped
with
continuous-track
traction
system.
The
goal
was
improve
performance
by
developing
mathematical
models
that
describe
robot’s
behavior,
which
were
validated
through
experimental
measurements.
incorporates
digital
twin
based
ROS
(Robot
Operating
System)
configure
nodes
responsible
navigation.
A
PID
controller
is
implemented
each
motor,
zero-pole
cancellation
achieve
first-order
dynamics,
and
anti-windup
prevent
integral
error
accumulation
when
reference
not
met.
Finally,
physical
implementation
carried
out
validate
functionality
proposed
results
demonstrated
ensured
precise
stable
navigation,
highlighting
effectiveness
approach
in
dynamic
environments.
This
work
contributes
advancing
robotic
controlled
environments
offers
potential
improving
teleoperation
systems
more
complex
scenarios.
Drones,
Journal Year:
2024,
Volume and Issue:
8(3), P. 95 - 95
Published: March 12, 2024
A
novel
reinforcement
deep
learning
deterministic
policy
gradient
agent-based
sliding
mode
control
(DDPG-SMC)
approach
is
proposed
to
suppress
the
chattering
phenomenon
in
attitude
for
quadrotors,
presence
of
external
disturbances.
First,
dynamics
model
quadrotor
under
study
derived,
and
problem
described
using
formulas.
Second,
a
controller,
including
its
surface
reaching
law,
chosen
nonlinear
dynamic
system.
The
stability
designed
SMC
system
validated
through
Lyapunov
theorem.
Third,
(RL)
agent
based
on
(DDPG)
trained
adaptively
adjust
switching
gain.
During
training
process,
input
signals
are
actual
desired
angles,
while
output
action
time-varying
Finally,
mentioned
above
utilized
as
parameter
regulator
facilitate
adaptive
adjustment
gain
associated
with
law.
simulation
results
validate
robustness
effectiveness
DDPG-SMC
method.
Electronics,
Journal Year:
2025,
Volume and Issue:
14(3), P. 601 - 601
Published: Feb. 3, 2025
This
paper
introduces
an
Evolutionary
Computing
Control
Strategy
(ECCS)
for
the
motion
control
of
nonholonomic
robots,
and
integrates
ordinary
differential
equation
(ODE)-based
kinematics
model
with
a
nonlinear
predictive
(NMPC)
strategy
particle-based
evolutionary
computing
(PEC)
algorithm.
The
ECCS
addresses
key
challenges
traditional
NMPC
controllers,
such
as
their
tendency
to
fall
into
local
optima
when
solving
optimization
problems,
by
leveraging
global
capabilities
computation.
Experiment
results
on
MATLAB
Simulink
platform
demonstrate
that
proposed
significantly
improves
accuracy
reduces
errors
compared
linearized
MPC
(LMPC)
strategies.
Specifically,
maximum
error
90.6%
94.5%,
mean
square
67.8%
92.6%,
root
43.5%
70.3%
in
velocity
steering
angle
control,
respectively.
Furthermore,
experiments
are
separately
implemented
CarSim
physical
environment
verify
availability
ECCS.
These
validate
effectiveness
embedding
ODE
framework
robust
efficient
robots.
A
novel
reinforcement
learning
deep
deterministic
policy
gradient
agent-based
sliding
mode
control
(DDPG-SMC)
approach
is
proposed
to
suppress
the
chattering
phenomenon
in
attitude
for
quadrotors,
presence
of
external
disturbances.
First,
dynamics
model
studied
quadrotor
derived
and
problem
described
by
formulas.
Second,
a
controller
including
its
surface
reaching
law
selected
nonlinear
dynamic
system,
stability
designed
SMC
system
supported
Lyapunov
theorem.
Third,
(RL)
agent
based
on
(DDPG)
trained
adjust
switching
gain
adaptively.
During
training
process,
input
signals
are
actual
desired
angles,
output
action
time-varying
gain.
Finally,
above
applied
as
parameter
regulator,
implement
adaptive
adjustment
related
law,
simulation
results
verify
robustness
effectiveness
DDPG-SMC
method.
