This paper is about the estimation of the elongation speed in a motorcycle electro-hydraulic suspension using a couple of accelerometers or a single sensor at wheel side. A Kalman filtering approach allows to successfully solve the estimation problem in both the cases, overcoming the vibrational disturbances which heavily affect the accelerometers on a sport motorbike. To be implemented on an off-the-shelf ECU, the Kalman observers need a low computational cost estimator of force. Therefore the secondary aim of the paper is to present a simplified model of the semi-active damper and compare it with a Neural Network based benchmark. Experimental results show that both velocity and force can be correctly estimated, also in the case of a single accelerometer with just a slight loss of performances.

Accelerometer-based estimation of the elongation speed in a motorcycle suspension via Kalman-filter techniques

DELVECCHIO, DIEGO;SPELTA, CRISTIANO;SAVARESI, SERGIO MATTEO
2010

Abstract

This paper is about the estimation of the elongation speed in a motorcycle electro-hydraulic suspension using a couple of accelerometers or a single sensor at wheel side. A Kalman filtering approach allows to successfully solve the estimation problem in both the cases, overcoming the vibrational disturbances which heavily affect the accelerometers on a sport motorbike. To be implemented on an off-the-shelf ECU, the Kalman observers need a low computational cost estimator of force. Therefore the secondary aim of the paper is to present a simplified model of the semi-active damper and compare it with a Neural Network based benchmark. Experimental results show that both velocity and force can be correctly estimated, also in the case of a single accelerometer with just a slight loss of performances.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/579202
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