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Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans

The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, r...

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Enregistré dans:
Format: Online
Langue:anglais
Publié: MDPI - Multidisciplinary Digital Publishing Institute 2022
Sujets:
Kalman filter
motion capture
gait analysis
inertial sensor
rail vehicles
track irregularities
multibody dynamics
inertial sensors
computer vision
singular configuration
parallel robot
motion control
3D tracking
screw theory
Kalman filtering
coupled states-inputs estimation
virtual sensors
slider-crank mechanism
virtual sensoring
physical sensors
smart/intelligent sensors
sensor technology and applications
sensing principles
signal processing in sensor systems
symbolic generation
real-time computation
human-in-the-loop
haptic devices
parameter estimation
curve fitting method
hydraulic system
predictive maintenance
characteristic curve
product life cycle
digital twin
adaptive Kalman filter
nonlinear models
virtual sensing
multibody based observers
vehicle dynamics estimation
sideslip angle estimation
factor graph
graphical models
movable repetitive lander
fault-tolerant soft-landing
landing configuration
stability optimization
n/a
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Accès en ligne:ONIX_20220111_9783036523576_844
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