Estimation of inertial characteristics of tumbling spacecraft using constant state filter

Abstract Reconstruction of dynamical parameters is one of the main challenges faced in the on-orbit servicing missions for defunct spacecraft. And the quaternion plays a major role in parameterizations of the dynamical model. In this paper, the analytical solution of the quaternion differential equations of a tumbling symmetrical object is derived. Given this solution, a constant state filter is proposed for inertial characteristics estimation and attitude prediction of tumbling spacecraft. The key idea of the present filter is to replace dynamic variables by the undetermined constant parameters of the analytical solution. These parameters are estimated during the filtering process and then used to calculate the dynamic variables of the spacecraft. Furthermore, they are also utilized to determine the inertial characteristics and predict the future attitude motions. Compared with traditional EKFs, the constant state filter shows good performance when the measurement sampling interval is large or a priori estimation of the state is unavailable, because the dynamic model and observation model are transformed into approximate linear forms by utilizing the constant state vector. Numerical simulations verify the convergence and precision of the proposed filter.

[1]  J.C.K. Chou,et al.  Quaternion kinematic and dynamic differential equations , 1992, IEEE Trans. Robotics Autom..

[2]  F. Markley Multiplicative Versus Additive Filtering for Spacecraft Attitude Determination , 2003 .

[3]  Zheng H. Zhu,et al.  Autonomous robotic capture of non-cooperative target using visual servoing and motion predictive control , 2014, Auton. Robots.

[4]  F. Markley,et al.  Unscented Filtering for Spacecraft Attitude Estimation , 2003 .

[5]  Shai Segal,et al.  Vision-based relative state estimation of non-cooperative spacecraft under modeling uncertainty , 2011, 2011 Aerospace Conference.

[6]  Zachary Manchester,et al.  Quaternion Variational Integrators for Spacecraft Dynamics , 2016 .

[7]  Satya N. Atluri,et al.  A Simple Collocation Scheme for Obtaining the Periodic Solutions of the Duffing Equation, and its Equivalence to the High Dimensional Harmonic Balance Method: Subharmonic Oscillations , 2012 .

[8]  Eric Martin,et al.  Autonomous capture of a tumbling satellite: Research Articles , 2007 .

[9]  Jianping Yuan,et al.  Half-order optimally scaled Fourier expansion method for solving nonlinear dynamical system , 2016 .

[10]  Charles D. Brown Elements of Spacecraft Design , 2002 .

[11]  Alvar Saenz-Otero,et al.  Visual-Inertial Estimation and Control for Inspection of a Tumbling Spacecraft: Experimental Results from the International Space Station , 2012 .

[12]  Roberto Opromolla,et al.  A Model-Based 3D Template Matching Technique for Pose Acquisition of an Uncooperative Space Object , 2015, Sensors.

[13]  Marco Martorella,et al.  Estimation of the total rotational velocity of a non-cooperative target using a 3D InISAR system , 2015, 2015 IEEE Radar Conference (RadarCon).

[14]  S. Tanygin,et al.  Mass Property Estimation Using Coasting Maneuvers , 1997 .

[15]  Steve Ulrich,et al.  Vision-Based Relative Navigation and Control for Autonomous Spacecraft Inspection of an Unknown Object , 2013 .

[16]  D. Scheeres Orbital motion in strongly perturbed environments , 2012 .

[17]  J.-Q. Yuan,et al.  A new method for solving the gyrodynamic equations using quaternions , 1984 .

[18]  Steven Dubowsky,et al.  State, shape, and parameter estimation of space objects from range images , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[19]  Frank Kirchner,et al.  GNC architecture for autonomous robotic capture of a non-cooperative target: Preliminary concept design , 2016 .

[20]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[21]  Shinichi Nakasuka,et al.  Precise attitude rate estimation using star images obtained by mission telescope for satellite missions , 2015 .

[22]  Farhad Aghili,et al.  Adaptive motion estimation of a tumbling satellite using laser-vision data with unknown noise characteristics , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Annalisa Fregolent,et al.  IDENTIFICATION OF RIGID BODY INERTIA PROPERTIES FROM EXPERIMENTAL DATA , 1996 .

[24]  Ioannis M. Rekleitis,et al.  Autonomous capture of a tumbling satellite , 2007, J. Field Robotics.

[25]  Farhad Aghili,et al.  A Prediction and Motion-Planning Scheme for Visually Guided Robotic Capturing of Free-Floating Tumbling Objects With Uncertain Dynamics , 2012, IEEE Transactions on Robotics.

[26]  John L. Crassidis,et al.  Survey of nonlinear attitude estimation methods , 2007 .

[27]  Mandyam V. Srinivasan,et al.  A Vision based system for attitude estimation of UAVS , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  T. A. Burton,et al.  Stability and Periodic Solutions of Ordinary and Functional Differential Equations , 1986 .

[29]  E. Wilson,et al.  On-line gyro-based, mass-property identification for thruster-controlled spacecraft using recursive least squares , 2002, The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002..

[30]  F Aghili,et al.  Fault-Tolerant Position/Attitude Estimation of Free-Floating Space Objects Using a Laser Range Sensor , 2011, IEEE Sensors Journal.

[31]  Fuzhen Zhang Matrix Theory: Basic Results and Techniques , 1999 .

[32]  Robert M. Sanner,et al.  Accurate State Estimation and Tracking of a Non-Cooperative Target Vehicle , 2006 .

[33]  Khanh Pham,et al.  On-Orbit Identification of Inertia Properties of Spacecraft Using a Robotic Arm , 2008 .