Hybrid Control of Space Manipulators for Active Debris Removal: A Comprehensive Review
- Paper ID
95234
- DOI
- author
- company
University of Strathclyde / Mechanical and Aerospace Engineering; University of Strathclyde
- country
United Kingdom
- year
2025
- abstract
Space debris poses a growing threat to operational spacecraft, requiring effective methods for debris removal. Space manipulators are applied for active debris removal (ADR) by capturing and de-orbiting the debris. Efficient control of space manipulator systems is required to ensure accurate trajectory tracking, collision-free and synchronised motion for the removal. In particular, for the unknown and tumbling debris, the complex dynamics of debris motion and the uncertainties of these dynamics and the environment are involved, making it difficult to effectively control space manipulators with single or conventional control methods for active debris removal (ADR). Therefore, this review paper aims to provide a comprehensive review and analysis of hybrid control approaches for space manipulators in ADR to address the above challenges, with a focus on integrating multiple control strategies to enhance the performance in terms of accuracy, efficiency and robustness of space manipulators. Hybrid control approaches usually combine conventional control methods, such as Proportional-Integral-Derivative (PID) control and Sliding Mode Control (SMC), with modern AI-based strategies such as Neural Networks (NN) and Fuzzy Logic Control (FLC) to address the complex dynamics of space manipulator systems. This review identifies the potential for hybrid control systems to provide superior adaptability, real-time performance, and robustness in uncertain environments. It investigates various hybrid control strategies, including Neural Network-Based Sliding Mode Control (NN-SMC), PID-Controlled Fuzzy Logic (PID-FLC), and Neuro-Fuzzy Control, and highlights their advantages in space debris removal. Additionally, this review discusses the integration of the Lidar and visual sensors and the potential for modern AI-based strategies to enhance the adaptability of hybrid controllers. Despite the promising performance, the challenges of computational complexity, real-time implementation, and robustness to space uncertainties remain for hybrid strategies. Therefore, this review concludes with a recommendation for future research directions, including the integration of different AI techniques, sensor systems, and optimisation techniques to enhance the performance of hybrid control systems for ADR. Hybrid control approaches reviewed in this paper can also be applied to in-orbit servicing, such as spacecraft maintenance, repair and assembly. It contributes to both academia and industry by providing a comprehensive overview of the state-of-the-art advancements in space manipulator hybrid control approaches, highlighting key methodologies, challenges, and potential solutions to improve space debris removal and in-orbit servicing.