Intelligent Hybrid Control for Free-Floating Space Robots: PSO–RL–SMC With Inverse-Dynamics Tracking

Abstract

Free-floating space robots must capture and manipulate targets without attitude anchoring, which makes precise motion control difficult due to base&#x2013;arm coupling, uncertain dynamics, and actuator limits. This work presents an integrated pipeline that addresses these challenges by combining particle swarm optimization to select a low-coupling initial posture, a reinforcement-learning policy to generate smooth approach motions, and a sliding-mode tracker mapped through free-floating inverse dynamics for robust execution. The method was evaluated on a seven-joint system with realistic inertial properties and viscous effects. Across complete capture runs, the controller achieved an overall root-mean-square tracking error of <inline-formula> <tex-math notation="LaTeX">$5.9133\times 10^{-3}$ </tex-math></inline-formula> rad, which corresponds to approximately 0.3389&#x00B0;, an overall steady-state mean-squared error of <inline-formula> <tex-math notation="LaTeX">$4.0954\times 10^{-6}$ </tex-math></inline-formula> rad2 (about 0.116&#x00B0; root-mean-square in the final window),and the torque norm bounded with a peak of approximately 40 N<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>m, demonstrating efficient and stable actuation. These results indicate consistent sub-degree accuracy, stable approach behavior, and effective suppress of base reactions, demonstrating that the proposed PSO&#x2013;RL&#x2013;SMC framework is a practical and high-precision solution for on-orbit target capture.