Reliable Kalman Filtering for Satellite Attitude Estimation Under Gyroscope Partial Failure

Adnane, Akram; Jiang, Hong; Mohammed, Mohammed Arezki Si; Bellar, A.; Ahmed‐Foitih, Zoubir

doi:10.1109/imcec.2018.8469422

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Another real-time geolocation solution of a radio frequency (RF) emitter was presented by Ellis and Dowla (2018) using a constrained Unscented Kalman Filter which reduced the convergence time, high resiliency to noise, sub-kilometer geolocation accuracies and the maintenance of stability. The presence of sensor faults also leads to failure of Kalman filter and hence Adnane et al, (2018) developed the Fault Tolerant Extended Kalman Filter in view of improving the state estimation reliability.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…( 29), the adaptive robust control law is designed as: here u ̅ a is the variable model reimbursed for attaining flawless tracking, and u ̅ s being a vigorous control function having the appearance of (31) where u ̅ s1 is for stabilizing the nominal system, ks1 is any positive definite diagonal matrix, u ̅ s2 = −k s2 (p)p being arobust feedback term used to diminish the result of uncertainties and k s2 (p) is a non-linear feedback gain. With reference to adaptive robust control law, the tracking error dynamics is formed as (32) Here, the application of the suggested controller as in above eq. ( 32) is plotted clearly in Fig.…”

Section: Advance Adaptive Robust Control Lawmentioning

confidence: 99%

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Extended Kalman Filter Based Estimations for Satellite Attitude Control System

Dube

Sharma

Patel

2021

IJEAT

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This paper mainly focuses on the maneuver of the satellite in orbit. A non-linear multi-inputs multi-outputs model has been derived from Newton-Euler equations of motion. The dynamics is presented with control methodologies allowing the Extended Kalman Filter (EKF) to iteratively provide improved data sets with zero errors. As the system is distracted from the atmospheric swings which are random hence the problem of stochastic disturbance is furnished. A set of differential equations of two dimensional Ito stochastic type is used for modeling the said disturbances (before t = 4s is recorded). The attitude parameters are recorded in RT-LAB setup with the Extended Kalman Filter (EKF) providing adequately superior estimation outcome which thereby makes the filter more appealing. With the presence of Gaussian noise in both dimension and system, Extended Kalman Filter gives the correct estimates. It’s collaboration with hardware setup RT-LAB is commendable. Hence, an Extended Kalman Filter which deals with such nonlinear models proves to be a higher choice for achieving best online results. A comparison reflecting the tracking and stable control of the satellite for the designed advanced adaptive robust controller (AARC) for two situations is plotted. The priority of making the system stable in the presence of stochastic disturbance is also visited. Also, the use of three different values of the confounding variables revealed that the control weighting line is completely diminished thereby boosting the tracking when the satellite is in orbit. Moreover, the previous research involves methods to improve satellite communication on ground station, this paper deals with exact positioning of concerned satellite attitude parameters and its validation tested experimentally on OPAL-RT hardware. To sum up, the development of advanced adaptive robust controllers have encouraged the stability and accuracy of systems considering the varying atmospheric conditions. The simulation results predict perfect tracking of output with respect to the desired set-point in the presence of stochastic disturbance for the proposed controller.

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Section: Extended Kalman Filtermentioning

confidence: 99%

Section: Advance Adaptive Robust Control Lawmentioning

confidence: 99%