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Investigation and Model-Based Compensation of the Pitch Dynamic Impact on Longitudinal Acceleration Measurement on Motorcycles
Technical Paper
2017-32-0053
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In this study we focus on systematic disturbances caused by the motorcycle pitch dynamic when measuring longitudinal acceleration on motorcycles using low-cost acceleration sensors. Major systematic influences in the sensor measurement like gravitational acceleration, suspension dynamics and the road slope are addressed. During acceleration phases the motorcycle pitch angle changes according to the suspension setting. As a result the longitudinal sensing axis of the accelerometer includes parts of the gravitational acceleration and lags parts of the longitudinal acceleration. Gravitational acceleration has also significant influence on inclined roads. To obtain correct values of the effective longitudinal acceleration, the disturbances in the measured signal are analyzed and in further consequence compensated. For this purpose a linearized in-plane-dynamics model of the motorcycle is derived from a comprehensive multibody simulation. The mathematical description of the motorcycle behavior includes the systematic influences and serves as a basis for the model-based compensation. A state observer for pitch angle estimation and road slope reconstruction is designed. As a result the measured acceleration can be corrected with the estimated quantities. In the course of ongoing development of motorcycle dynamics control and advancement of drivetrains, e.g. hybridization, independent and economically measureable reference signals are required to achieve desired vehicle behavior with closed-loop control. Acceleration measurement on two-wheelers can be achieved at low hardware cost and is a promising quantity for innovative control designs.
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Winkler, A. and Grabmair, G., "Investigation and Model-Based Compensation of the Pitch Dynamic Impact on Longitudinal Acceleration Measurement on Motorcycles," SAE Technical Paper 2017-32-0053, 2017, https://doi.org/10.4271/2017-32-0053.Data Sets - Support Documents
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