AI-Augmented Model Predictive Control for Safe and Adaptive Rendezvous and Proximity Operations
Robotics
Summary
The authors developed a smart control system for spacecraft to safely and efficiently approach and operate near potentially hostile satellites. Their system uses a method called Model Predictive Control (MPC) that plans moves ahead while adjusting itself based on the situation, ensuring safety and respecting physical limits. Instead of directly deciding thruster commands, their system tweaks understandable control settings to adapt. They tested this approach in a space simulation game with adversarial scenarios and found it improved safety and performance compared to fixed controls. This creates an adaptable and clear way to guide spacecraft during close maneuvers in challenging conditions.
Authors
Luca Sportelli, Tyler Barr, Cagri Kilic, Di Wu
Abstract
Autonomous rendezvous and proximity operations (RPO) in adversarial orbital environments require guidance architectures balancing target pursuit, safety preservation, and real-time adaptability under dynamically evolving interaction conditions. Although learning-based approaches show promise, their application to safety-critical orbital robotics remains limited by concerns regarding interpretability, robustness, and constraint awareness. This work presents an adaptive Model Predictive Control (MPC) framework for autonomous spacecraft RPO in multi-agent adversarial scenarios. The proposed architecture combines a constrained receding-horizon MPC formulation with a data-driven supervisory tuning layer that adjusts controller parameters from offline closed-loop evaluation and online interaction geometry. Relative motion follows Clohessy-Wiltshire (CW) dynamics, enabling computationally efficient finite-horizon prediction and real-time quadratic optimization. The MPC formulation incorporates actuator limits, predictive keep-out-zone constraints, slack-variable feasibility handling, and optional Control Barrier Function (CBF) safety filtering. Rather than generating thrust commands directly, the adaptive layer modifies interpretable MPC parameters, including tracking weights, safety penalties, minimum-separation objectives, and keep-out-zone objectives. The framework was evaluated in the official Kerbal Space Program Differential Game (KSPDG) Capture-the-Satellite environment through Monte Carlo simulations. Results demonstrate improved closed-loop robustness, adaptive maneuvering behavior, and rendezvous performance compared with fixed-parameter MPC while preserving safety-aware operation and real-time feasibility, providing a modular, interpretable foundation for adaptive spacecraft RPO.