Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans
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| Autore: |
Cuadrado Javier
|
| Titolo: |
Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans
|
| Pubblicazione: | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica: | 1 electronic resource (256 p.) |
| Soggetto topico: | Technology: general issues |
| Soggetto non controllato: | 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 | |
| Persona (resp. second.): | NayaMiguel |
| CuadradoJavier | |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910557337203321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |