A soft stroke sensor for motorcycle rear suspension. (August 2017)

• Record Type: Journal Article
• Authors: Capriglione, D.; Carratù, M.; Liguori, C.; Paciello, V.; Sommella, P.
• Is Part Of: Measurement. Volume 106(2017)
• Page Start: 46
• Page End: 52
• Publication Date: 2017-08
• Subjects: Artificial neural networks -- Software sensor -- Instrument fault detection; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2017.04.011
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Semi-active suspension control needs to measure the relative velocity of the wheels respect to the vehicle body to regulate the damping forces. Linear potentiometers are the most used sensors in racing for linearity and simplicity, but they are expensive for mass market and unreliable in the long run. A solution to limit the cost of the vehicle electronics is the suitable exploitation of the analytical redundancy among the measured quantities of interest. The paper describes the application of a systematic approach to the optimum design of a soft displacement sensor, which may be adopted with the rear suspension of the two-wheeled vehicles. Focus is mainly devoted to the real-time exploitation of recurrent Artificial Neural Network within a typically adopted instrumentation set. Experimental results concerning with the soft sensor enable to develop an effective Instrument Fault Detection and Isolation scheme in order to improve the system reliability.