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The term mass properties refers to the set of parameters that describes the distribution of mass in a rigid body. These parameters include moments of inertia, products of inertia, mass, and center of mass. It is common for some of these parameters to be measured relative to the spacecraft’s manufacturing coordinate system before a spacecraft launched and for all to be predicted for the on-orbit lifetime. However, not all are measured, and the predictions may include assumptions (for example, about propellant usage) that do not match the real system to some extent. Such information can be valuable, for example, in verifying the success of a deployment maneuver which is predicted to change the inertia matrix in a certain way. It can also be indispensable in diagnosing anomalous performance, characterizing broken appendages, or recovering from an unanticipated loss of attitude control. Knowledge of the mass properties can help operators select optimized control-system gains to improve performance or recover the spacecraft. Knowing the alignment and other parameters of an attitude control system’s actuators and sensors has similar benefits. The problem of estimating sensor alignments relative to some preferred coordinate system is beyond the scope of this book, and it has been treated successfully in work by Schuster (Shuster et al., J Astron Sci 39(4):519, 1991; Shuster and Pitone, J Astronaut Sci 39:547, 1991), Pittelkau (J Guidance Control Dyn 24(6):1187, 2001), and Crassidis (Crassidis et al., J Guidance Control Dyn 30(1):12, 2007); J.L. Crassidis, M.S. Whorton, Multi-user system for earth sensing spacecraft attitude calibration using international space station attitude information, in AIAA Scitech 2020 Forum (2020), p. 1607), among many authors. This chapter focuses on estimating the alignment of momentum-control system parameters, i.e. RWA and CMG alignments, including approaches to estimate mass properties and MCS parameters simultaneously. They are presented as simple batch or recursive least-squares solutions, although Kalman filters, and even more sophisticated schemes involving machine learning certainly can be integrated into these fundamental results.

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In-Flight Estimation of Spacecraft and Momentum-Control System Parameters

  • 章节

  • 发表日期：

    2025年

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  • Frederick A. Leve,
  • Mason A. Peck,
  • Brian J. Hamilton,
  • William Bialke

摘要

The term mass properties refers to the set of parameters that describes the distribution of mass in a rigid body. These parameters include moments of inertia, products of inertia, mass, and center of mass. It is common for some of these parameters to be measured relative to the spacecraft’s manufacturing coordinate system before a spacecraft launched and for all to be predicted for the on-orbit lifetime. However, not all are measured, and the predictions may include assumptions (for example, about propellant usage) that do not match the real system to some extent. Such information can be valuable, for example, in verifying the success of a deployment maneuver which is predicted to change the inertia matrix in a certain way. It can also be indispensable in diagnosing anomalous performance, characterizing broken appendages, or recovering from an unanticipated loss of attitude control. Knowledge of the mass properties can help operators select optimized control-system gains to improve performance or recover the spacecraft. Knowing the alignment and other parameters of an attitude control system’s actuators and sensors has similar benefits. The problem of estimating sensor alignments relative to some preferred coordinate system is beyond the scope of this book, and it has been treated successfully in work by Schuster (Shuster et al., J Astron Sci 39(4):519, 1991; Shuster and Pitone, J Astronaut Sci 39:547, 1991), Pittelkau (J Guidance Control Dyn 24(6):1187, 2001), and Crassidis (Crassidis et al., J Guidance Control Dyn 30(1):12, 2007); J.L. Crassidis, M.S. Whorton, Multi-user system for earth sensing spacecraft attitude calibration using international space station attitude information, in AIAA Scitech 2020 Forum (2020), p. 1607), among many authors. This chapter focuses on estimating the alignment of momentum-control system parameters, i.e. RWA and CMG alignments, including approaches to estimate mass properties and MCS parameters simultaneously. They are presented as simple batch or recursive least-squares solutions, although Kalman filters, and even more sophisticated schemes involving machine learning certainly can be integrated into these fundamental results.