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ISA Trans. 2018 Feb;73:268-277. doi: 10.1016/j.isatra.2017.12.013. Epub 2018 Jan 3.

Online Wavelet Complementary velocity Estimator.

Author information

1
Università degli studi di Bergamo, Department of Engineering and Applied Sciences, Mechatronics Laboratory, Italy. Electronic address: paolo.righettini@unibg.it.
2
Università degli studi di Bergamo, Department of Engineering and Applied Sciences, Mechatronics Laboratory, Italy. Electronic address: roberto.strada@unibg.it.
3
Università degli studi di Bergamo, Department of Engineering and Applied Sciences, Mechatronics Laboratory, Italy. Electronic address: ehsan.khademolama@unibg.it.
4
Università degli studi di Bergamo, Department of Engineering and Applied Sciences, Mechatronics Laboratory, Italy. Electronic address: shirin.valilou@unibg.it.

Abstract

In this paper, we have proposed a new online Wavelet Complementary velocity Estimator (WCE) over position and acceleration data gathered from an electro hydraulic servo shaking table. This is a batch estimator type that is based on the wavelet filter banks which extract the high and low resolution of data. The proposed complementary estimator combines these two resolutions of velocities which acquired from numerical differentiation and integration of the position and acceleration sensors by considering a fixed moving horizon window as input to wavelet filter. Because of using wavelet filters, it can be implemented in a parallel procedure. By this method the numerical velocity is estimated without having high noise of differentiators, integration drifting bias and with less delay which is suitable for active vibration control in high precision Mechatronics systems by Direct Velocity Feedback (DVF) methods. This method allows us to make velocity sensors with less mechanically moving parts which makes it suitable for fast miniature structures. We have compared this method with Kalman and Butterworth filters over stability, delay and benchmarked them by their long time velocity integration for getting back the initial position data.

KEYWORDS:

Complementary filter; Data fusion; Intelligent sensors; Sensor integration; State estimation; Velocity estimation; Wavelet filter banks

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