Compensation control of commercial vehicle platoon considering communication delay and response lag
Computers & Electrical Engineering,
Год журнала:
2024,
Номер
119, С. 109623 - 109623
Опубликована: Сен. 7, 2024
Язык: Английский
An Efficient Coordinated Observer LQR Control in a Platoon of Vehicles for Faster Settling Under Disturbances
World Electric Vehicle Journal,
Год журнала:
2025,
Номер
16(1), С. 28 - 28
Опубликована: Янв. 7, 2025
The
rapid
proliferation
of
vehicles
globally
presents
significant
challenges
to
road
transportation
efficiency
and
safety,
including
accidents,
emissions,
energy
utilization,
management.
Autonomous
vehicle
platooning
emerges
as
a
promising
solution
within
intelligent
systems,
offering
benefits
like
reduced
fuel
consumption
optimized
use.
However,
implementing
autonomous
faces
obstacles
such
stability
under
disturbances,
safety
protocols,
communication
networks,
precise
control.
This
paper
proposes
novel
control
strategy
coordinated
Kalman
observer–Linear
Quadratic
Regulator
(CKO-LQR)
ensure
platoon
formation
in
the
presence
disturbances.
disturbances
considered
include
movements,
sensor
noise,
delays,
with
leading
vehicle’s
movement
serving
commanding
signal.
proposed
controller
maintains
constant
inter-gap
distance
between
despite
utilizing
observer
estimate
preceding
movements.
A
comparative
analysis
conventional
PID
controllers
demonstrates
superior
performance
terms
faster
settling
times
robustness
against
research
contributes
enhancing
systems.
Язык: Английский
Disturbance and uncertainty compensation control for heterogeneous platoons under network delays
Computers & Electrical Engineering,
Год журнала:
2025,
Номер
123, С. 110066 - 110066
Опубликована: Янв. 24, 2025
Язык: Английский
Longitudinal motion control algorithm for autonomous vehicles taking decisions based on the preceding vehicle behavior pattern
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 4, 2025
In
the
field
of
autonomous
driving,
a
key
concern
is
whether
driving
algorithms
can
better
adapt
to
their
environments.
Currently,
vehicles
often
adopt
single
control
strategy,
which
reduce
traffic
efficiency
and
negatively
impact
other
road
users.
To
address
this
issue,
paper
presents
longitudinal
motion
algorithm
for
that
makes
decisions
based
on
preceding
vehicle’s
behavior
pattern,
aiming
comprehensively
improve
both
safety.
Firstly,
using
NGSIM
dataset,
large
number
kinematic
features
from
highway-driving
are
extracted
standardized.
Subsequently,
Principal
Component
Analysis
(PCA)
applied
dimensionality
decouple
data.
Following
this,
Fuzzy
C-Means
clustering
(FCM)
employed
categorize
vehicles’
characteristics
into
several
typical
patterns.
By
incorporating
regulations
various
countries,
external
metrics
established
evaluate
results.
Based
these
metrics,
parameters
optimized
enhance
reliability
outcomes.
Additionally,
vehicle
pattern
identification
module
was
developed
lightweight
Convolutional
Neural
Network
(CNN),
achieving
high
accuracy
low
computational
load
in
online
experiments.
Depending
different
patterns
vehicle,
we
design
safety
distance
model
balances
efficiency.
ensure
target
following
met,
Deep
Reinforcement
Learning
(DRL)
developed.
Finally,
comparative
experiments
conducted,
results
demonstrate
proposed
effectively
optimizes
efficiency,
safety,
comfort
comprehensive
manner,
thereby
verifying
its
feasibility.
Язык: Английский
Coordinated Control of Autonomous Electric Vehicles With Lateral and Longitudinal Control Using a Hybrid Approach
Journal of Field Robotics,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 8, 2025
ABSTRACT
The
rise
of
Autonomous
Electric
Vehicles
(AEVs)
has
presented
formidable
challenges
in
the
automotive
sector,
demanding
advanced
sensor
technology,
intricate
control
systems,
and
sophisticated
decision‐making
algorithms.
Due
to
inherently
nonlinear
dynamics
uncertainties
associated
with
these
vehicles,
conventional
methods
fall
short
providing
robust
solutions.
This
study
proposes
a
hybrid
approach
for
coordinated
longitudinal
lateral
autonomous
driving
scenarios.
Addressing
control,
research
integrates
road
geometry
considerations.
Utilizing
Proportional
Integral
Derivative
(PID)
controller
Fire
Hawk
Optimizer
(FHO)
algorithm.
optimizes
gains
Nonlinear
dynamics,
ensuring
reliable
speed
tracking.
Additionally,
Linear
Parameter
Varied‐Models
Predictive
Controller
(LPV‐MPC)
addresses
related
time‐varying
speeds
distance
impact
on
vehicle
stability.
Implementation
matrix
laboratory
demonstrates
approach's
superiority
terms
speed,
precision,
stability,
trajectory
tracking,
achieving
minimal
error
0.0526
mean
error,
absolute
root
squared
0.193,
0.087
0.108
respectively.
Язык: Английский
Method for Helicopter Turboshaft Engines Controlling Energy Characteristics Through Regulating Free Turbine Rotor Speed and Fuel Consumption Based on Neural Networks
Energies,
Год журнала:
2024,
Номер
17(22), С. 5755 - 5755
Опубликована: Ноя. 18, 2024
This
research
is
devoted
to
the
development
of
a
method
for
helicopter
turboshaft
engine
energy
characteristics
control
by
regulating
free
turbine
rotor
speed
and
fuel
consumption
using
neural
network
technologies.
A
mathematical
model
was
created
that
links
main
parameters,
based
on
which
relation
with
output
power
established.
In
this
research,
differential
equation
obtained
consumption,
power,
speed,
makes
it
possible
monitor
dynamics
in
various
operating
modes.
controller
developed
neuro-fuzzy
processes
input
data,
including
desired
current
allows
real-time
adjustments
improve
operational
efficiency.
flight
data
analysis
during
Mi-8MTV
TV3-117
test,
improved
signal
processing
quality
due
time
sampling
adaptive
quantisation
methods
(this
confirmed
assessing
homogeneity
representativeness
training
test
datasets).
comparative
traditional
controllers
showed
use
reduces
transient
process
8.92%
while
increasing
accuracy
F1
score
18.28%
21.32%,
respectively.
Язык: Английский