Space GNSS Receiver Performance Results With Precise Real-Time On-board Orbit Determination (P2OD) in LEO Missions

2019 ◽  
Author(s):  
J.M. Palomo ◽  
P. D'angelo ◽  
P. F. Silva ◽  
A.J. Fernández ◽  
P. Giordano ◽  
...  
Keyword(s):  
Real Time ◽  
Orbit Determination ◽  
Gnss Receiver ◽  
Receiver Performance ◽  
Performance Results

Design and development of a backstepping controller autopilot for fixed-wing UAVs

2021 ◽  
pp. 1-27
Author(s):  
D. Sartori ◽  
F. Quagliotti ◽  
M.J. Rutherford ◽  
K.P. Valavanis
Keyword(s):  
Real Time ◽  
Control Law ◽  
Parametric Uncertainties ◽  
Hardware In The Loop ◽  
Aircraft Control ◽  
Backstepping Approach ◽  
Small Uav ◽  
Backstepping Controller ◽  
Definition Of ◽  
Performance Results

Abstract Backstepping represents a promising control law for fixed-wing Unmanned Aerial Vehicles (UAVs). Its non-linearity and its adaptation capabilities guarantee adequate control performance over the whole flight envelope, even when the aircraft model is affected by parametric uncertainties. In the literature, several works apply backstepping controllers to various aspects of fixed-wing UAV flight. Unfortunately, many of them have not been implemented in a real-time controller, and only few attempt simultaneous longitudinal and lateral–directional aircraft control. In this paper, an existing backstepping approach able to control longitudinal and lateral–directional motions is adapted for the definition of a control strategy suitable for small UAV autopilots. Rapidly changing inner-loop variables are controlled with non-adaptive backstepping, while slower outer loop navigation variables are Proportional–Integral–Derivative (PID) controlled. The controller is evaluated through numerical simulations for two very diverse fixed-wing aircraft performing complex manoeuvres. The controller behaviour with model parametric uncertainties or in presence of noise is also tested. The performance results of a real-time implementation on a microcontroller are evaluated through hardware-in-the-loop simulation.

Processing Panorama Video in Real-time

2014 ◽  
Vol 08 (02) ◽  
pp. 209-227 ◽  
Author(s):  
Håkon Kvale Stensland ◽  
Vamsidhar Reddy Gaddam ◽  
Marius Tennøe ◽  
Espen Helgedagsrud ◽  
Mikkel Næss ◽  
...  
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Low Cost ◽  
Video Camera ◽  
Wide Field ◽  
High Data ◽  
Data Rates ◽  
Camera Arrays ◽  
Analysis System ◽  
Performance Results

There are many scenarios where high resolution, wide field of view video is useful. Such panorama video may be generated using camera arrays where the feeds from multiple cameras pointing at different parts of the captured area are stitched together. However, processing the different steps of a panorama video pipeline in real-time is challenging due to the high data rates and the stringent timeliness requirements. In our research, we use panorama video in a sport analysis system called Bagadus. This system is deployed at Alfheim stadium in Tromsø, and due to live usage, the video events must be generated in real-time. In this paper, we describe our real-time panorama system built using a low-cost CCD HD video camera array. We describe how we have implemented different components and evaluated alternatives. The performance results from experiments ran on commodity hardware with and without co-processors like graphics processing units (GPUs) show that the entire pipeline is able to run in real-time.

Comparison of the real-time Precise Orbit Determination for LEO between Kinematic and Reduced-Dynamic modes

Measurement ◽  
2021 ◽  
pp. 110224
Author(s):  
Zhiyu Wang ◽  
Zishen Li ◽  
Liang Wang ◽  
Ningbo Wang ◽  
Yang Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Orbit Determination ◽  
Precise Orbit Determination ◽  
The Real ◽  
Precise Orbit ◽  
Reduced Dynamic

From Science to Practice: Improving ROP by Utilizing a Cloud-Based Machine-Learning Solution in Real-Time Drilling Operations

2021 ◽  
Author(s):  
Kriti Singh ◽  
Sai Yalamarty ◽  
Curtis Cheatham ◽  
Khoa Tran ◽  
Greg McDonald
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Situational Awareness ◽  
Field Tests ◽  
Field Trials ◽  
Permian Basin ◽  
Lateral Vibrations ◽  
Drilling Parameters ◽  
Drilling Conditions ◽  
Performance Results

Abstract This paper is a follow up to the URTeC (2019-343) publication where the training of a Machine Learning (ML) model to predict rate of penetration (ROP) is described. The ML model gathers recent drilling parameters and approximates drilling conditions downhole to predict ROP. In real time, the model is run through an optimization sweep by adjusting parameters which can be controlled by the driller. The optimal drilling parameters and modeled ROP are then displayed for the driller to utilize. The ML model was successfully deployed and tested in real time in collaboration with leading shale operators in the Permian Basin. The testing phase was split in two parts, preliminary field tests and trials of the end-product. The key learnings from preliminary field tests were used to develop an integrated driller's dashboard with optimal drilling parameters recommendations and situational awareness tools for high dysfunction and procedural compliance which was used for designed trials. The results of field trials are discussed where subject well ROP was improved between 19-33% when comparing against observation/control footage. The overall ROP on subject wells was also compared against offset wells with similar target formations, BHAs, and wellbore trajectories. In those comparisons against qualified offsets, ROP was improved by as little as 5% and as much as 33%. In addition to comparing ROP performance, results from post-run data analysis are also presented. Detailed drilling data analytics were performed to check if using the recommendations during the trial caused any detrimental effects such as divergence in directional trends or high lateral or axial vibrations. The results from this analysis indicate that the measured downhole axial and lateral vibrations were in the safe zone. Also, no significant deviations in rotary trends were observed.

Application of SVD-ACKF algorithm for real-time orbit determination in optoelectronic theodolite

2017 ◽  
Vol 46 (1) ◽  
pp. 117005
Author(s):  
李兆铭 Li Zhaoming ◽  
杨文革 Yang Wenge ◽  
丁丹 Ding Dan ◽  
王超 Wang Chao
Keyword(s):  
Real Time ◽  
Orbit Determination
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