scholarly journals A robust observer based on energy-to-peak filtering in combination with neural networks for parameter varying systems and its application to vehicle roll angle estimation

Mechatronics ◽  
2018 ◽  
Vol 50 ◽  
pp. 196-204 ◽  
Author(s):  
B.L. Boada ◽  
M.J.L. Boada ◽  
V. Diaz
Keyword(s):  
Neural Networks ◽  
Roll Angle ◽  
Angle Estimation ◽  
Robust Observer ◽  
Parameter Varying

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

scholarly journals Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2188 ◽  
Author(s):  
Javier García Guzmán ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Montalvo Martínez ◽  
María L. Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Angle Estimation

Roll angle estimation for smart munitions under GPS jamming environment

2008 ◽  
Vol 41 (2) ◽  
pp. 9499-9504 ◽  
Author(s):  
Han Sung Lee ◽  
HeeYoung Park ◽  
KwangJin Kim ◽  
Jang Gyu Lee ◽  
Chan Gook Park
Keyword(s):  
Roll Angle ◽  
Angle Estimation

Vehicle sideslip angle estimation using nonlinear parameter-varying observers

2015 ◽  
Author(s):  
Boulaid Boulkroune ◽  
Sebastiaan V. Aalst ◽  
Erwin Rademakers ◽  
Koen Sannen ◽  
Norddin El Ghouti
Keyword(s):  
Nonlinear Parameter ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
Parameter Varying

Roll angle estimation in two-wheeled vehicles

2009 ◽  
Vol 3 (1) ◽  
pp. 20-32 ◽  
Author(s):  
I. Boniolo ◽  
M. Tanelli ◽  
S.M. Savaresi
Keyword(s):  
Roll Angle ◽  
Angle Estimation ◽  
Wheeled Vehicles

Fault detection in roll angle estimation for two-wheeled vehicles* *Corresponding author: Cristiano Spelta, e-mail: [email protected]

2012 ◽  
Vol 45 (24) ◽  
pp. 54-59 ◽  
Author(s):  
Simone Formentin ◽  
Ivo Boniolo ◽  
Paolo Lisanti ◽  
Cristiano Spelta ◽  
Sergio M. Savaresi
Keyword(s):  
Fault Detection ◽  
Roll Angle ◽  
Angle Estimation ◽  
Wheeled Vehicles ◽  
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