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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Bibliographische Detailangaben
Format:	Online
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Schlagworte:	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
Online-Zugang:	ONIX_20220111_9783036523576_844
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Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans

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