scholarly journals Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0257849
Author(s):  
Muhammad Wasim ◽  
Ahsan Ali ◽  
Mohammad Ahmad Choudhry ◽  
Faisal Saleem ◽  
Inam Ul Hasan Shaikh ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Precision Agriculture ◽  
Model Uncertainty ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Wind Disturbance ◽  
Model Uncertainties ◽  
Nonlinear Simulation ◽  
Lumped Model ◽  
Disturbance Estimation

An airship is lighter than an air vehicle with enormous potential in applications such as communication, aerial inspection, border surveillance, and precision agriculture. An airship model is made up of dynamic, aerodynamic, aerostatic, and propulsive forces. However, the computation of aerodynamic forces remained a challenge. In addition to aerodynamic model deficiencies, airship mass matrix suffers from parameter variations. Moreover, due to the lighter-than-air nature, it is also susceptible to wind disturbances. These modeling issues are the key challenges in developing an efficient autonomous flight controller for an airship. This article proposes a unified estimation method for airship states, model uncertainties, and wind disturbance estimation using Unscented Kalman Filter (UKF). The proposed method is based on a lumped model uncertainty vector that unifies model uncertainties and wind disturbances in a single vector. The airship model is extended by incorporating six auxiliary state variables into the lumped model uncertainty vector. The performance of the proposed methodology is evaluated using a nonlinear simulation model of a custom-developed UETT airship and is validated by conducting a kind of error analysis. For comparative studies, EKF estimator is also developed. The results show the performance superiority of the proposed estimator over EKF; however, the proposed estimator is a bit expensive on computational grounds. However, as per the requirements of the current application, the proposed estimator can be a preferred choice.

Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

2021 ◽  
pp. 1-29
Author(s):  
Xiongbin Peng ◽  
Yuwu Li ◽  
Wei Yang ◽  
Akhil Garg
Keyword(s):  
Kalman Filter ◽  
Working Conditions ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Influential Factor ◽  
Recursive Least Squares ◽  
Maximum Deviation ◽  
Soc Estimation ◽  
Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

scholarly journals Orbit Propagation and Determination of Low Earth Orbit Satellites

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ho-Nien Shou
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Nonlinear Problems ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

scholarly journals State Estimation for Sampled-Data Descriptor Nonlinear System: A Strong Tracking Unscented Kalman Filter Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tiantian Liang ◽  
Mao Wang ◽  
Zhenhua Zhou
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Descriptor System ◽  
Sampled Data ◽  
Filtering Method ◽  
System A ◽  
Filter Approach ◽  
Fading Factor

This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman filter (STUKF) algorithm is designed for the state estimation. Then, a defined suboptimal fading factor is proposed and added to the prediction covariance for decreasing the weight of the prior knowledge on the conventional UKF filtering solution. Finally, a simulation example is given to show the effectiveness of the proposed method.

A Method for Real-Time Estimating Pose of Large Aircraft Component in Process of Pose Adjustment Based on the Unscented Kalman Filter

2012 ◽  
Vol 442 ◽  
pp. 251-255
Author(s):  
Zheng Ying
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Measuring Point ◽  
Aircraft Component ◽  
Forward Kinematic ◽  
Large Aircraft

To estimate the pose of large aircraft component in pose adjustment quickly and accurately, a real-time estimation method based on Unscented Kalman filter (UKF) is proposed. Firstly, in the process of the aircraft component adjustment, a rough value of aircraft component’s pose is acquired by using forward kinematic model and the displacement of positioners on real time. Then, position of a measuring point fixed on aircraft component is obtained by a laser tracker. At last, UKF is employed to integrate the previous rough value and the measuring point position for evaluating the accurate pose of aircraft component. Numerical simulation results show that the presented method is achieved easily, calculated fast and high accurate.

scholarly journals Disturbance Estimation and Wave Filtering Using an Unscented Kalman Filter

2016 ◽  
Vol 49 (23) ◽  
pp. 518-523 ◽  
Author(s):  
Stefan Wirtensohn ◽  
Michael Schuster ◽  
Johannes Reuter
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Disturbance Estimation

scholarly journals Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

2021 ◽  
Vol 11 (24) ◽  
pp. 11797
Author(s):  
Dongdong Ge ◽  
Zhendong Zhang ◽  
Xiangdong Kong ◽  
Zhiping Wan
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Stress Test ◽  
Estimation Method ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Recursive Least Squares ◽  
Primary Concern ◽  
Soc Estimation

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

Multi-phase flow measurement in a gas refinery using decentralized Lyapunov–based adaptive observer

2020 ◽  
pp. 014233122096497
Author(s):  
Abolfazl Varvani Farahani ◽  
Mohsen Montazeri
Keyword(s):  
Kalman Filter ◽  
Gas Flow ◽  
Oil And Gas ◽  
Unscented Kalman Filter ◽  
Adaptive Observer ◽  
Actual Process ◽  
Lumped Model ◽  
Multi Phase Flow ◽  
Multi Phase ◽  
Gas Refinery

This paper presents a new Lyapunov-based nonlinear adaptive observer and joint unscented Kalman filter to precisely estimate the states and the parameters for the low-order lumped model of the multi-phase flow at the gas refinery. The main focus of the study is to estimate the discharge coefficient of the orifice meter installed on the interconnection lines of the subsystems (parameters) and the total oil and gas mass flows (states). The adaptive estimation is conducted using the real-time measurements including choke pressure, bottom line pressure, single-phase gas flow, and single-phase liquid flow in the refinery outlet. To check the stability and performance of the system against changes, the Lyapunov theory has been used. In all stages, the investigations were based on the data collected from the actual process in the South Pars Gas Complex, Iran. Using the dynamic HYSYS simulation, it is found that the proposed adaptive observer is capable of estimating the oil and gas flows and identifying the discharge coefficient of the flow meter at issue. To show the performance of the proposed adaptive observer, it is evaluated against and compared with the unscented Kalman filter. The comparison of the results obtained from the proposed observer, unscented Kalman filter, and dynamic HYSYS simulation with data collected from the actual process of the refinery shows the appropriate performance of the both estimation algorithms in detecting the changes in liquid and gas flow rates and the consistency of their results with the real process in the South Pars Gas Complex. The simulations reveal that low-order lumped model is sufficient for estimation of parameters and states of the multiphase flow entering the gas refinery.

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