Spacecraft Uncooperative Rendezvous & Docking Control

Abstract

The Model Predictive Control (MPC) method provides a control strategy providing inherent robustness and improved capabilities in handling constraints while guaranteeing the satisfac- tion of the control objective. In recent years, Model Predictive Control (MPC) has started to see more application for the purpose of Rendezvous and Docking (RVD) with an uncoopera- tive target. This is both due to the increased capability of online solver algorithms and the increased interest and need for autonomous RVD to an uncooperative target for Active De- bris Removal (ADR) and in orbit servicing. Acknowledging the potential applications of these developments, this thesis seeks to close the gap between research and real-time application of an MPC aimed at minimizing propellant consumption for such a RVD mission. In this thesis, an MPC strategy with the aim of decreasing propellant consumption is developed for an RVD mission. First, an overview will be given of some related orbital theory and the three relative orbital dynamics models, which will be used as prediction models for the MPC strategies. These models are the Clohessy Wiltshire (HCW) model, Xu-Wang model in Cartesian coor- dinates, model and STM developed by Yamanaka and Ankersen in ROE. An overview will be provided on the available MPC strategies developed until now. Second, the various MPC formulations that will be evaluated, for each mission phase, and their related constraints and cost function are presented. Next, the results of the all the MPC formulations for the different mission phases are presented. Finally, it is shown that the use of more accurate prediction models does not add any significant benefit in terms of propellant consumption and error for all mission phases. However, the use of robust techniques and the inclusion of disturbances in the prediction models is shown to have a clear benefit for all prediction models in terms of propellant consumption and error. The use of incremental input cost function is not shown to consistently improve the propellant use and error. It is determined that a robust MPC for- mulation using the HCW model with a disturbance estimator provides the best performance in terms of propellant use and error from far range to proximity operations.