Abstract
The moment of inertia parameters play a critical role in assuring the spacecraft mission throughout its lifetime. However, determination of the moment of inertia is a key challenge in operating satellites. During satellite mission, those parameters can change in orbit for many reasons such as sloshing, fuel consumption, etc. Therefore, the inertia matrix should be estimated in orbit to enhance the attitude estimation and control accuracy. This paper investigates the use of gyroscope to estimate the attitude rate and inertia matrix for low earth orbit satellite via extended Kalman filter. Simulation results show the effectiveness and advantages of the proposed algorithm in estimating these parameters without knowing the nominal inertia. The robustness of the proposed algorithm has been validated using the Monte-Carlo method. The obtained results demonstrate that the accuracy of the estimated inertia and angular velocity parameters is satisfactory for satellite with coarse accuracy mission requirements. The proposed method can be used for different types of satellites.
Highlights
Spacecraft inertia parameters are prone to unpredictable changes throughout a mission due to many reasons such as fuel consumption, fuel sloshing, deployment failures, collision with unexpected object, damage, connection with other parts, deorbiting, structure changes or any events which substantially influence the inertia matrix (Kim et al 2010; 2016; Manchester and Peck 2017)
Several researches and engineering projects have been proposed to estimate full components of inertia matrix, which consist of moment of inertia (MOI) and product of inertia (POI) elements
We have presented an algorithm to estimate the moment of inertia, product of inertia elements and angular velocity based on extended Kalman filter using only gyroscope measurement applied to a LEO microsatellite
Summary
Spacecraft inertia parameters are prone to unpredictable changes throughout a mission due to many reasons such as fuel consumption, fuel sloshing, deployment failures, collision with unexpected object, damage, connection with other parts, deorbiting, structure changes or any events which substantially influence the inertia matrix (Kim et al 2010; 2016; Manchester and Peck 2017). In Bordany et al (2000), a recursive least square (RLS) procedure is proposed to identify satellite inertia matrix and thruster parameters in orbit for mini-satellite UoSAT-12 built by Surrey Satellite Technology Ltd. In Psiaki (2005), an algorithm is developed using the inner and outer least-squares optimizations to estimate the dynamic model parameters. Keim et al (2006) have presented an inertia estimation technique based on a constraint least squares minimization problem with LMIs (linear matrix inequalities). Most of these algorithms require integrals or derivatives of measured spacecraft state variables as input, necessitating prefiltering that introduces additional complexity and numerical error
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
