A Comparative Study of MPC and Reinforcement Learning Control for Space Robotics Manipulators

Abstract

This paper explores and evaluates both traditional and learning-based methods for spacecraft control and coupled robotic arm manipulation in a microgravity environment. Space junk, debris, and out-of-control satellites currently floating in orbit pose a severe risk to critical space assets, necessitating debris removal systems. Controlling a robotic arm and base vehicle in zero-gravity environments poses a significant challenge due to the inherent physical coupling of the system’s dynamics. This paper is concerned with a 3-DOF planar spacecraft with a 2-DOF manipulator approaching and intercepting a rotating object in 2D space. It establishes a cascaded Proportional-Integral-Derivative (PID) controller as a baseline benchmark before focusing on comparing optimal and learning-based con-trol strategies, specifically a unified 5-Degree-of-Freedom non-linear Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL). The analysis evaluates steady-state precision, dynamic stability, and approach trajectories to determine the operational suitability of each methodology for target interception and maintained contact.