Volume 14, Issue 4 (Journal of Control, V.14, N.4 Winter 2021)                   JoC 2021, 14(4): 25-41 | Back to browse issues page

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Sabzevari S, Vali A R, Arvan M R, Dehghan S M, Ferdowsi M H. Comparison Performance of Stochastic Estimators and Symmetry-Preserving Observer to Determine Nanosatellite Attitude with a Single-Sensor Magnetometer. JoC 2021; 14 (4) :25-41
URL: http://joc.kntu.ac.ir/article-1-662-en.html
1- Malek Ashtar University of Technology
Abstract:   (5349 Views)
Designing the estimator that can determine the attitude with a single sensor is vital due to the limited weight and volume in the nano-satellite, the problems caused by the limited lifetime of the mechanical gyroscope in the long term and the eclipse phenomenon. To compensate for data deficiency, a two-nested filter has been utilized in this paper. To this end, the attitude in the second filter is estimated using the sensor data and the magnetic field derivative estimation from the first filter by the extended Kalman filter. Two stochastic algorithms named as multiplicative extended Kalman filter and square-root unscented quaternion estimator are compared with the proposed symmetry-preserving nonlinear observer in order to obtain an appropriate accuracy for determining the attitude of the nano-satellite, which has only a three-axis magnetometer. The proposed method is based on invariant observers under the action of the Lie group. The moving frame approach has been used so that the observer's parameters can be adjusted through the invariant error dynamic equations. Simulation results confirm an acceptable accuracy in all three algorithms for both time and frequency response analyses. However, the root mean square error of the attitude error with a nonlinear observer is much less than the stochastic algorithm in case of a larger initial estimation error. Furthermore, this approach guarantees convergence by the Lyapunov stability proof owing to setting the parameters with periodic differential Riccati equations.
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Type of Article: Review paper | Subject: Special
Received: 2019/04/18 | Accepted: 2020/01/12 | ePublished ahead of print: 2020/10/5 | Published: 2021/02/19

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