Estimating wind using a quadrotor

The aim of this work is to estimate the average wind influencing a quadrotor drone only based on standard navigation sensors and equations of motion. It can be used in several situation, including atmospheric studies, trajectory planning under environmental constraints, or as a reference for studying flights in shear layer. For this purpose, a small quadrotor drone with spherical shape has been developed. Flight data are recorded from telemetry during indoor and outdoor flight tests and are post-processed. The proposed solution is based on a calibration procedure with global optimization to extract the drag model and a Kalman Filter for online estimation of the wind speed and direction. Finally, an on-board implementation of the real-time estimation is demonstrated with real flights in controlled indoor environment.

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A machine for real-time estimation of the tribological characteristics of materials

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-4-61-65 ◽

2020 ◽

Vol 86 (4) ◽

pp. 61-65

Author(s):

M. V. Abramchuk ◽

R. V. Pechenko ◽

K. A. Nuzhdin ◽

V. M. Musalimov

Keyword(s):

Real Time ◽

Time Estimation ◽

Tribological Characteristics ◽

Continuous Processing ◽

Friction Machine ◽

Dynamic Friction Coefficient ◽

Displacement Sensors ◽

Frictional Interaction ◽

Real Time Estimation ◽

Automated Quality Control

A reciprocating friction machine Tribal-T intended for automated quality control of the rubbing surfaces of tribopairs is described. The distinctive feature of the machine consists in implementation of the forced relative motion due to the frictional interaction of the rubbing surfaces fixed on the drive and conjugate platforms. Continuous processing of the signals from displacement sensors is carried out under conditions of continuous recording of mutual displacements of loaded tribopairs using classical approaches of the theory of automatic control to identify the tribological characteristics. The machine provides consistent visual real time monitoring of the parameters. The MATLAB based computer technologies are actively used in data processing. The calculated tribological characteristics of materials, i.e., the dynamic friction coefficient, damping coefficient and measure of the surface roughness, are presented. The tests revealed that a Tribal-T reciprocating friction machine is effective for real-time study of the aforementioned tribological characteristics of materials and can be used for monitoring of the condition of tribo-nodes of machines and mechanisms.

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Onboard Real-time Estimation of Mean Orbital Elements with Atmospheric Drag

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2013.01722 ◽

2013 ◽

Vol 39 (10) ◽

pp. 1722

Author(s):

Zhao-Wei SUN ◽

Wei-Chao ZHONG ◽

Shi-Jie ZHANG ◽

Jian ZHANG

Keyword(s):

Real Time ◽

Time Estimation ◽

Atmospheric Drag ◽

Orbital Elements ◽

Real Time Estimation

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Soft sensor for real-time estimation of tablet potency in continuous direct compression manufacturing operation

International Journal of Pharmaceutics ◽

10.1016/j.ijpharm.2021.120624 ◽

2021 ◽

Vol 602 ◽

pp. 120624

Author(s):

Reza Kamyar ◽

David Lauri Pla ◽

Anas Husain ◽

Giuseppe Cogoni ◽

Zilong Wang

Keyword(s):

Real Time ◽

Time Estimation ◽

Soft Sensor ◽

Direct Compression ◽

Real Time Estimation

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A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States

Sensors ◽

10.3390/s21124068 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4068

Author(s):

Zheshuo Zhang ◽

Jie Zhang ◽

Jiawen Dai ◽

Bangji Zhang ◽

Hengmin Qi

Keyword(s):

Unscented Kalman Filter ◽

Time Estimation ◽

Estimation Algorithm ◽

Estimation Methods ◽

Fusion Algorithm ◽

State Variable ◽

Current Estimation ◽

Real Time Estimation ◽

And Control ◽

Independent Parameters

Vehicle parameters are essential for dynamic analysis and control systems. One problem of the current estimation algorithm for vehicles’ parameters is that: real-time estimation methods only identify parts of vehicle parameters, whereas other parameters such as suspension damping coefficients and suspension and tire stiffnesses are assumed to be known in advance by means of an inertial parameter measurement device (IPMD). In this study, a fusion algorithm is proposed for identifying comprehensive vehicle parameters without the help of an IPMD, and vehicle parameters are divided into time-independent parameters (TIPs) and time-dependent parameters (TDPs) based on whether they change over time. TIPs are identified by a hybrid-mass state-variable (HMSV). A dual unscented Kalman filter (DUKF) is applied to update both TDPs and online states. The experiment is conducted on a real two-axle vehicle and the test data are used to estimate both TIPs and TDPs to validate the accuracy of the proposed algorithm. Numerical simulations are performed to further investigate the algorithm’s performance in terms of sprung mass variation, model error because of linearization and various road conditions. The results from both the experiment and simulation show that the proposed algorithm can estimate TIPs as well as update TDPs and online states with high accuracy and quick convergence, and no requirement of road information.

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