Airship aerodynamic model estimation using unscented Kalman filter

Wasim, Muhammad; Ali, Ahsan

doi:10.23919/jsee.2020.000102

Cited by 29 publications

(16 citation statements)

References 14 publications

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“…Building on the existing literature, in this paper, an estimation solution is proposed that introduces a lumped model uncertainties estimation approach based on the UKF estimator. The proposed work is a significant extension of our previous results reported in [27,28]. The previous work only deals with the estimation of an airship aerodynamic model; however, the proposed work extends the results and incorporates model uncertainties and wind disturbances at the same time.…”

Section: Introductionmentioning

confidence: 56%

“…They have estimated the aerodynamic coefficients or the complete aerodynamic model. In these estimation methods, Kalman filters have been applied to estimate aerodynamic model coefficients [26] or aerodynamic forces and torques [27,28]. The aerodynamic model parameter estimation method estimates more than 50 parameters [26].…”

Section: Introductionmentioning

confidence: 99%

“…This is directly related to the Kalman filter computational complexity. However, the method suggested by some researchers introduces only six augmented state variables to estimate the complete aerodynamic model [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

et al. 2021

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“…In order to prevent false detections, the following processing should be done: calculate the theoretical thread roll’s margin according to thread consumption speed and time interval. This result is the estimated value, the above-detected value based on computer vision is the measured value, and the two results are fused as the final result of thread roll’s margin through a Kalman Filter [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ].

…”

Section: Proposed Methodsmentioning

confidence: 99%

The Detection of Thread Roll’s Margin Based on Computer Vision

Shi

Wang

2021

Sensors

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The automatic detection of the thread roll’s margin is one of the kernel problems in the textile field. As the traditional detection method based on the thread’s tension has the disadvantages of high cost and low reliability, this paper proposes a technology that installs a camera on a mobile robot and uses computer vision to detect the thread roll‘s margin. Before starting, we define a thread roll‘s margin as follows: The difference between the thread roll‘s radius and the bobbin’s radius. Firstly, we capture images of the thread roll‘s end surface. Secondly, we obtain the bobbin’s image coordinates by calculating the image’s convolutions with a Circle Gradient Operator. Thirdly, we fit the thread roll and bobbin’s contours into ellipses, and then delete false detections according to the bobbin’s image coordinates. Finally, we restore every sub-image of the thread roll by a perspective transformation method, and establish the conversion relationship between the actual size and pixel size. The difference value of the two concentric circles’ radii is the thread roll’s margin. However, there are false detections and these errors may be more than 19.4 mm when the margin is small. In order to improve the precision and delete false detections, we use deep learning to detect thread roll and bobbin’s radii and then can calculate the thread roll’s margin. After that, we fuse the two results. However, the deep learning method also has some false detections. As such, in order to eliminate the false detections completely, we estimate the thread roll‘s margin according to thread consumption speed. Lastly, we use a Kalman Filter to fuse the measured value and estimated value; the average error is less than 5.7 mm.

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“…Seo et al [12] solved aerodynamic parameter identification under abnormal flight conditions using UKF, considering hysteresis effects. Muhammad and Ahsan [13] used UKF for aerodynamic parameter identification of airships and compared it with the identification results of EKF, indicating that the identification accuracy of UKF is better than that of EKF.…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Aerodynamic Parameters of Experimental Rockets Based on Unscented Kalman Filtering

Tang,

Jiang

2024

International Journal of Aerospace Engineering

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Online identification of aerodynamic parameters of experimental rockets was completed based on unscented Kalman filtering (UKF). Numerical simulation, hardware-in-the-loop (HIL) simulation, and flight tests were conducted. The identification error of aerodynamic force in numerical simulation and HIL simulation is within 2%. For flight test data, trajectory reconstruction was performed using the identified aerodynamic forces, and the results showed that the identification results were more accurate than the interpolation table calculation results. The flight test identification results show that the identification method can complete parameter online identification under the conditions of limited performance of onboard computers, real sensor errors, and servo response. The approximate linear correlation between α and δe and the reason for their formation from the moment balance were analyzed. It was pointed out that when the recognition sampling period is long, this phenomenon will affect the identification of parameters, and a solution is proposed.

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Airship aerodynamic model estimation using unscented Kalman filter

Cited by 29 publications

References 14 publications

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

Unscented Kalman filter for airship model uncertainties and wind disturbance estimation

The Detection of Thread Roll’s Margin Based on Computer Vision

Online Identification of Aerodynamic Parameters of Experimental Rockets Based on Unscented Kalman Filtering

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