ESA GNC Conference Papers Repository

On-orbit servicing (OOS) technology refers to the use of spacecraft or robots to service, repair or upgrade satellites while they are still in orbit. This technology has the potential to extend the lifespan of satellites by allowing them to be serviced and repaired instead of being immediately replaced once they malfunction or run out of fuel. However, this technology is complex and challenging in many ways. From an Attitude and Orbit Control System (AOCS)/Guidance, Navigation, and Control (GNC) perspective, OOS missions are particularly demanding due to the time-varying and coupled flexible dynamics of the system. Due to these challenges, the success of on-orbit servicing missions is constrained by the ability to accurately model the system and use advanced analysis tools to predict worst-case scenarios during a preliminary design phase [1]. This is critical as it helps ensure that the control system and other mission critical components are able to handle the unique and complex dynamics of the system, and that any potential issues are identified and addressed before the mission is undertaken. Without these accurate models and analysis tools, it would be difficult to ensure its success. For that reason, the Two-Input Two-Output Ports (TITOP) approach [2] was used to build a Linear Parameter-Varying (LPV) model of two complex multibody mechanical systems, while keeping the uncertain nature of the plant and condensing all possible mechanical configurations in a single low order linear fractional representation (LFR). This framework is a multi-body approach which can connect multiple flexible sub-structures through dynamic ports. It considers uncertain parameters and all possible configurations (changes in mechanical properties, changes in geometric configurations like the rotation angle of the solar panel, changes in reaction wheel speed, etc.) in a single LFR. The models built with the TITOP approach are then ready for robust control synthesis as well as robust stability and performance assessment. These models have been implemented in the latest version of the Satellite Dynamics Toolbox (SDT) [3], which enables users to build models of flexible spacecraft. In this context, this paper presents a comprehensive approach for planning and designing an on-orbit servicing mission scenario from an end-to-end perspective, taking into account the structure and control aspects. The authors aim to address a significant gap in the literature by considering the challenges of flexibility and system uncertainty in the design of a robust controller for an orbital servicing operation. As on-orbit operations of large and flexible structures become more common in future space missions, this approach is increasingly relevant for ensuring the success of these kinds of scenarios. The scenario chosen to demonstrate the capabilities of the proposed approach is a system composed of two spacecraft, a chaser and a target, both with large flexible solar arrays. The target, also designated as client satellite, is considered collaborative and designed with specific features to facilitate rendezvous and capture. The main focus of this paper is on the target manipulation phase. The latter consists of the chaser manipulating the target by means of a robotic arm with the objective of performing maintenance or to use the target as a mission extension pod. It should be noted that this is just one example scenario, and the same approach can be applied to explore a multitude of other mission concepts such as on-orbit assembly. Parametric uncertainty is taken into account on the inertial and mass properties of the target spacecraft, as well as on the natural frequencies of the solar arrays’ flexible modes. Since the final goal is to design a controller where the bandwidth of interest can be highly impacted by these parameters, it is of paramount importance that these effects are taken into account. In addition, the attitude control system must also be robust to the time-varying configurations of the flexible solar arrays and chaser’s arm manipulator, since these have a direct influence on the dynamic behaviour of the system. For that reason, the system is also parameterized according to these rotation angles. Ultimately, this paper is organized into three parts: system modeling, controller design and stability and performance analysis. In the first part, a symbolic linear model for control synthesis is obtained using SDT. The result is an LFR, minimal in terms of parameter occurrences, which takes into account all parametric uncertainties, varying geometrical configurations, large changes in inertia and flexibility. The proposed modeling design is verified using a non-linear physics simulator built with the Simscape multibody toolset from Mathworks. A thorough analysis of the system dynamics is also performed, both in the time and frequency domain. In the second part of the paper, the SDT model is used to design and optimize a controller capable of complying with the performance requirements which are imposed as constraints on the feedback loop. Finally, the third part details the rigorous analysis procedure that was used to obtain robust performance and robust stability certificates. [1] R. Rodrigues, V. Preda, F. Sanfedino, and D. Alazard, “Modeling, robust control synthesis and worst-case analysis for an on-orbit servicing mission with large flexible spacecraft,” Aerospace Science and Technology, vol. 129, p. 107865, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S1270963822005399 [2] D. Alazard, J. A. Perez, C. Cumer, and T. Loquen, “Two-input two-output port model for mechanical systems,” in AIAA Guidance, Navigation, and Control Conference, 2015, p. 1778. [3] D. Alazard and F. Sanfedino, Satellite Dynamics Toolbox User’s Guide, 2021, available at https://nextcloud. isae.fr/index.php/s/oPQjcytZMxL27a5.