Real Time Roll Angle Estimation for Fast Spinning Projectiles

2018 ◽  
Author(s):  
Serta� Erdemir ◽  
K. Can Tasan
Keyword(s):  
Real Time ◽  
Roll Angle ◽  
Angle Estimation

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 estimation method of roll angle based on least squares recursion

2020 ◽  
Vol 306 ◽  
pp. 05002
Author(s):  
Chen Jingsheng ◽  
Li Chuanjun ◽  
Hu Peisen
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Least Squares Method ◽  
Estimation Method ◽  
Time Estimation ◽  
Calculation Procedure ◽  
Roll Angle ◽  
Angle Estimation ◽  
Real Time Estimation ◽  
High Dynamic

The rotary missile stands a high overload during the launch and has to be powered up after launch, so it is necessary to achieve inflight alignment under high dynamic conditions. As a key technology of inflight alignment, the measurement method of roll angle has attracted more and more attention from researchers. The rotational speed of the rotary missile is very high, and most MIMUs cannot directly measure the roll angle. To solve this problem, this paper proposes a roll angle estimation method based on least squares method, analyzes its principle and derives the calculation procedure. Then on this basis, the roll angle estimation method based on least squares recursion is studied. The principle and calculation procedure of this method are deduced in detail. At last, the simulation experiment on MATLAB is carried out. The results show that this method is simple in calculation, high in accuracy and good in real-time performance, and has great application value.

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

Rollover Warning of Articulated Vehicles Based on a Time-To-Rollover Metric

1999 ◽  
Author(s):  
Bo-Chiuan Chen ◽  
Huei Peng
Keyword(s):  
Real Time ◽  
The Other ◽  
Roll Angle ◽  
Vehicle Speed ◽  
Time Model ◽  
Whole Process ◽  
Articulated Vehicles ◽  
Articulated Vehicle ◽  
The One ◽  
Accuracy Problem

Abstract A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated vehicle. Ideally, a TTR metric will accurately “count-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately indicated. To implement TTR in real-time, there are two conflicting requirements. On the one hand, a faster-than-real-time model is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the whole process is illustrated in a design example. First, a simple yet reasonably accurate yaw/roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simplified real-time model. The NN takes the TTR generated by the simplified model, vehicle roll angle and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achievable across all the driving scenarios we tested.

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