Model Predictive Control for Autonomous Docking Maneuvers: Validation through Numerical Simulations and Experimental Tests on an Air-Bearing Facility

The increasing complexity of nanosatellite missions demands reliable and intelligent control systems capable of autonomously managing proximity operations. Among various control approaches, Model Predictive Control (MPC) offers an effective framework for handling multivariable dynamics and physical constraints in real time. This work investigates the application of MPC to autonomous docking maneuvers, combining numerical simulations and experimental validation on the SPARTANS air-bearing facility at the University of Padua. A custom three-degree-of-freedom (3-DoF) satellite module was developed, featuring eight compressed-air thrusters arranged to provide full translational and rotational actuation. The project encompassed the mechanical and pneumatic design of the module, the development of the MPC algorithm, the implementation of thruster actuation via Pulse Width Modulation (PWM), and the establishment of a real-time communication framework between the onboard microcontrollers and the tracking system. Comparative analysis between numerical simulations and laboratory experiments enabled the assessment of the controller’s performance in terms of trajectory accuracy, robustness, and disturbance rejection. The results confirm the feasibility and effectiveness of MPC for autonomous docking operations and highlight the potential of air-bearing testbeds as valuable experimental platforms for validating advanced Guidance, Navigation, and Control (GNC) methodologies in aerospace engineering.