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Autonomous robotic satellite capture using constrained predictive control

This thesis investigates the use of model-based predictive control for the capture of a multidegree-of freedom object that moves in a somewhat arbitrary manner, using a deployable manipulator. While the study is conducted through both computer simulation and ground-based experimental investigation, the intended application is focused on automating the robotic capture of a free-floating and spinning satellite. The main motivation for this application comes from the fact that robotic satellite capture in space, for retrieval, correction, repair, etc., can significantly eliminate the risks involved when astronauts are used in space walk scenarios to manually execute the necessary tasks. When a satellite is spinning, the maneuvering of a robotic manipulator by a human operator for a successful capture becomes increasing difficult. For this reason, satellite capturing using an autonomous robot is particularly attractive, and is studied in the thesis. The present investigation uses an innovative manipulator known as the Multi-module Deployable Manipulator System ( M D M S ) , which has been designed and built in our laboratory. It is a multipurpose manipulator, which consists of a combination of revolute and prismatic joints and provides several advantages over the standard, all-revolute-joint manipulators that are used in space applications. The advantages include reduced dynamic coupling, fewer configuration singularities, easier obstacle avoidance, and simpler inverse kinematics, when compared to all-revolutejoint manipulators of the same size and degrees of freedom. Furthermore, our experience in designing, controlling, and experimentation with two models of the manipulator provides an added incentive for using the M D M S as the test bed for the present investigation. In the controller that is developed, computer-simulated, implemented, and tested in the present research, the future path of the target satellite is predicted, and the controller uses this future knowledge of the target, and an internal model of the manipulator, to make optimal control decisions to minimize the tracking error between the target and the end-effector. Multi-parametric Quadratic Programming (mp-QP) techniques are used in order to obtain constrained optimal control decisions in real-time. The mp-QP algorithm offers fast solution times by explicitly solving the constrained quadratic programming problem offline and then using look-up tables in the realtime application. Unfortunately, the mp-QP solution is susceptible to numerical problems, and as a result the fully constrained predictive controller could not be implemented in real time. However, a sub-optimal version of the controller was implemented. The results show that the mp-QP