AI-Augmented Model Predictive Control for Safe and Adaptive Rendezvous and Proximity Operations

2026-07-18

AI Summary

This work addresses the challenges of safety, adaptability, and real-time performance in autonomous spacecraft rendezvous and proximity operations within adversarial orbital environments. The authors propose a novel framework that integrates model predictive control (MPC) with a data-driven parameter-tuning mechanism. Rather than directly generating control commands, the approach dynamically adjusts MPC parameters using online geometric information and incorporates predicted keep-out zone constraints, slack variables, and optional control barrier functions (CBFs) to enable adaptive decision-making while preserving interpretability and safety guarantees. Built upon the Clohessy–Wiltshire dynamics and formulated as a finite-horizon quadratic program, the system is validated through Monte Carlo simulations in the KSPDG “Capture-the-Satellite” scenario. Results demonstrate significant improvements over fixed-parameter MPC in closed-loop robustness, rendezvous performance, and maneuver adaptability, all while meeting real-time computational requirements.

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.

Problem

• rendezvous and proximity operations
• adversarial orbital environments
• safety preservation
• real-time adaptability
• autonomous spacecraft

Innovation

• Adaptive Model Predictive Control
• Rendezvous and Proximity Operations
• Control Barrier Function
• Data-driven Supervisory Tuning
• Clohessy-Wiltshire Dynamics