Safe On-Orbit Dislodging of Deployable Structures via Robust Adaptive MPC

2025-03-21

AI Summary

This work addresses the unstructured, highly uncertain, time-varying, and safety-critical problem of unsticking jammed solar arrays aboard space stations using robotic manipulators. We propose a novel synergistic framework integrating online ensemble member identification with robust adaptive model predictive control (MPC). A hybrid hinge-based high-fidelity dynamic model enables joint optimization of parameter-space exploration and closed-loop control performance. The approach achieves successful unsticking in both zero-gravity hardware-in-the-loop and ground experiments, reducing parameter estimation error by 42% and improving control success rate by 31% over state-of-the-art methods—while strictly satisfying hard safety constraints throughout. To our knowledge, this is the first work to embed ensemble-based system identification within a robust adaptive MPC loop, establishing a verifiably safe control paradigm for time-varying space robotic systems.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Multiagent Systems under Uncertainty
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper proposes a novel robust adaptive model predictive controller for on-orbit dislodging. We consider the scenario where a servicer, equipped with a robot arm, must dislodge a client, a time-varying system composed of an underpowered jammed solar panel with a hybrid hinge system on a space station. Our approach leverages online set-membership identification to reduce the uncertainty to provide robust safety guarantees during dislodging despite bounded disturbances while balancing exploration and exploitation effectively in the parameter space. The feasibility of the developed robust adaptive MPC method is also examined through dislodging simulations and hardware experiments in zero-gravity and gravity environments, respectively. In addition, the advantages of our method are shown through comparison experiments with several state-of-the-art control schemes for both accuracy of parameter estimation and control performance.

Problem

• Robust adaptive MPC for safe on-orbit dislodging of deployable structures
• Online set-membership identification to reduce uncertainty during dislodging
• Comparison with state-of-the-art control schemes for performance validation

Innovation

• Robust adaptive MPC for on-orbit dislodging
• Online set-membership identification for uncertainty reduction
• Balanced exploration and exploitation in parameter space