Spacecraft Proximity Operations via Tube-based Robust Model Predictive Control with Additive Disturbances

Paper number

IAC-17,C1,5,3,x37611

Author

Ms. Martina Mammarella, Politecnico di Torino, Italy

Coauthor

Dr. Elisa Capello, Politecnico di Torino, Italy

Coauthor

Dr. Hyeongjun Park, Naval Postgraduate School, United States

Coauthor

Prof. Giorgio Guglieri, Italy

Coauthor

Prof. Marcello Romano, United States

Year

2017

Abstract

Proximity maneuvers of two autonomous spacecraft have been extensively studied in the past years, in which both the strict
requirements in terms of spacecraft dynamics variations and the limitations due to the actuation system have taken into account.
Since the phase of proximity involves a rapid evolution of the kinematics and dynamics, linear control methodologies have
limited performance achievement. In particular, rendezvous and docking (RVD) problems require high level of robustness
in order to ensure the satisfaction of the mission constraints for any modeled possible disturbance and uncertainty that can
affect the system. For this reason, Tube-based Robust MPC represents an appealing strategy to handle disturbances and to
ensure robust constraints satisfaction. In our paper, a suitable strategy of control via Tube-based Robust MPC, in which
the robustness of the system is guaranteed in presence of additive disturbances, is proposed. The idea of this research is to
reduce the computational effort adopting a time-varying control law in which the feedback gain matrix is evaluated off-line.
A Linear Matrix Inequalities (LMI) approach is applied to the state feedback stabilization criterion for the stability analysis
and the evaluation of the feedback matrix. The experimental verification has been carried out considering the final phase
of the rendezvous and docking maneuver and using two Floating Spacecraft Simulators (FSS) that float over a polished
granite monolith surface reproducing a quasi-frictionless motion. A Vicon motion capture system provides accurate position
and orientation data and a discrete time Kalman filter processes the data and provides a full state estimate. An extensive
verification campaign, both in simulation and experimentation on this physical test bed, has been accomplished to validate
the performance of the control system and its compatibility for real-time implementation. Thus, the efficiency of the control
system is measured comparing the control effort and the computational time (in terms of CPU time).

Abstract document

IAC-17,C1,5,3,x37611.brief.pdf

Manuscript document

IAC-17,C1,5,3,x37611.pdf (🔒 authorized access only).

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