Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems

被引：16
作者：
Capriglione, Domenico [1 ]
Carratu, Marco [1 ]
Pietrosanto, Antonio [1 ]
Sommella, Paolo [1 ]
机构：
[1] Univ Salerno, Dept Ind Engn, I-84084 Fisciano, Italy
来源：
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2020年 / 69卷 / 05期
关键词：
Artificial neural network (ANN); fault-tolerant systems; microcontroller unit (MCU); nonlinear autoregressive with exogenous inputs (NARX); online; real time; NEURAL-NETWORK; DIAGNOSIS; MODEL; ARCHITECTURE; STATE;
D O I：
10.1109/TIM.2019.2963552
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：2367 / 2376
页数：10
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