The purpose of this work is to investigate the performance of the kinematic Kalman filter (KKF). In order to measure angular acceleration with a reliable and cost-effective method, Micro-Electro-Mechanical System MEMS accelerom- eters have been used. The estimated velocity has been implemented based on the model-based state estimation theory in order to compare the results with the KKF. In the model-based approaches, model parameters and external dis- turbance must be accurately known for the estimate of velocity to be accurate, which is very difficult in reality. The most attractive feature of KKF is that it is insensitive to modeling uncertainties and parameter variations
Velocity estimation and motion control using mems accelerometer
Fardin, Luca
2011/2012
Abstract
The purpose of this work is to investigate the performance of the kinematic Kalman filter (KKF). In order to measure angular acceleration with a reliable and cost-effective method, Micro-Electro-Mechanical System MEMS accelerom- eters have been used. The estimated velocity has been implemented based on the model-based state estimation theory in order to compare the results with the KKF. In the model-based approaches, model parameters and external dis- turbance must be accurately known for the estimate of velocity to be accurate, which is very difficult in reality. The most attractive feature of KKF is that it is insensitive to modeling uncertainties and parameter variationsFile | Dimensione | Formato | |
---|---|---|---|
Tesi_Luca_Fardin.pdf
accesso aperto
Dimensione
11.04 MB
Formato
Adobe PDF
|
11.04 MB | Adobe PDF | Visualizza/Apri |
The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License
https://hdl.handle.net/20.500.12608/14526