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Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans
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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, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product.
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Keywords
- 3D tracking
- adaptive Kalman filter
- characteristic curve
- Computer vision
- coupled states-inputs estimation
- curve fitting method
- Digital twin
- factor graph
- fault-tolerant soft-landing
- gait analysis
- graphical models
- Haptic devices
- human-in-the-loop
- hydraulic system
- inertial sensor
- inertial sensors
- Kalman filter
- Kalman filtering
- landing configuration
- Motion Capture
- motion control
- movable repetitive lander
- multibody based observers
- multibody dynamics
- n/a
- nonlinear models
- parallel robot
- Parameter estimation
- physical sensors
- predictive maintenance
- Product life cycle
- rail vehicles
- real-time computation
- screw theory
- sensing principles
- sensor technology and applications
- sideslip angle estimation
- signal processing in sensor systems
- singular configuration
- slider-crank mechanism
- smart/intelligent sensors
- stability optimization
- symbolic generation
- Technology, engineering, agriculture
- Technology: general issues
- track irregularities
- vehicle dynamics estimation
- virtual sensing
- virtual sensoring
- virtual sensors
Links
DOI: 10.3390/books978-3-0365-2358-3Editions