Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems

• This article describes the development and experimental verification of an instrument fault accommodation (IFA) scheme for front and rear suspension stroke sensors in motorcycles equipped with electronically controlled semiactive suspension systems. In particular, the IFA scheme is based on the use of nonlinear autoregressive with exogenous inputs (NARX) neural networks (NNs) employed as soft sensors for feeding the suspension control strategy back with measurement even in the presence of faults occurred on the sensors. Different NN architectures have been trained and tuned by considering real data acquired during several measurement campaigns. The performance has been compared with that of the well-known half-car model (HCM). Very satisfying results allow the soft sensor to be really integrated into fault-tolerant control systems. In experimental road tests, an implementation of the proposed IFA scheme on a low-cost microcontroller for automotive applications showed to be in real time. In this article, these experimental results are shown to prove the good performance of the IFA scheme in different motorcycle operating conditions. © 1963-2012 IEEE.

Nyckelord

Artificial neural network (ANN)

fault-tolerant systems

microcontroller unit (MCU)

nonlinear autoregressive with exogenous inputs (NARX)

online

real time

Automobile suspensions

Model automobiles

Motorcycles

Vehicle performance

Automotive applications

Experimental verification

Fault tolerant control systems

Measurement campaign

Neural networks (NNS)

Non-linear autoregressive with exogenous

Operating condition

Semi-active suspension systems

Suspensions (components)

Publikations- och innehållstyp

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