Speaker
Description
Introduction
IOANT is an on-going activity supported by the European Space Agency aimed at the development of a versatile autonomous GNC system for in-orbit assembly (IOA) of large antennas.
The target is to advance the Technology Readiness Level of several key GNC technologies to 4. To reach this, the activity is structured into two phases:
Phase 1 consists in the definition of a feasible end-to-end in-orbit assembly scenario, including vehicles, mechanisms, and interfaces. A complete GNC solution will be developed and validated using an integrated, coherent, and incremental detailed design, validation, and verification approach.
Phase 2 will advance the GNC solution by designing and performing a series of end-to-end proof of concept tests in a suitable ground facility, using a scaled-down version of the scenario studied in phase 1 and scaling back up the conclusions.
The technologies proposed to be studied can be applied to the assembly of a wide variety of antenna missions, also telescopes and, in a more general sense, mission where the assembly of structures is present.
This presentation discusses: the proposed scenarios – mission, system, and the GNC architecture challenges and proposed concepts; details and justifies the potential architectures and design solutions; and outlines prospective methodologies for prototyping, verification, and validation. The emphasis of this presentation is on the proposed design and not on results or analysis.
Motivation and challenge
Enhancements in the communications capacity of future satellites can be achieved by increasing the reflector surface area, producing narrower beams. Current and projected missions rely on foldable or deployable antenna concepts to provide larger reflective surfaces, but these systems are limited by the launcher capabilities both in terms of mass and volume.
A natural solution to the limitations imposed by a single launch is to split the payload into several launches, and then perform assembly operations in orbit. This is becoming possible in part thanks to the latest advances in on-orbit servicing and are the key to unlock the future of communications in space.
In-orbit assembly presents several benefits. It permits the construction of structures that do not fit into a single launcher, even opening the door to structures that could not be created on ground because of the limitations imposed by gravity. It can also increase the longevity and reliability of missions by replacing faulty, damaged, or outdated elements. There are also potential cost-savings, by reducing the mass required for protection and deployment mechanisms, and also reducing ground testing.
However, in-orbit assembly also presents a number of challenges that are currently being studied, both in terms of the system and in terms of the GNC. From the point of view of the system, such a mission requires multiple vehicles to transport and assemble modular segments; it is also expected to have contacts between several elements, which requires the use of manipulators and locking and interlocking mechanisms and introduce disturbances during the assembly process. The main GNC challenges appear in the Control department; the structures will experience large changes in physical properties as the assembly goes on, particularly in the mass-inertia (MCI) properties and the flexible properties. The developed control system needs to take those variations into account and be robust to uncertainties.
GNC architecture and design
The proposed mission and system definition step includes the establishment of needs and characteristics: 1) for large orbital antennas, through a survey of and subsequent perimeter definition for the IOA system paired with a representative End-to-End (E2E) scenario for which to tailor a flexible solution; and 2) for the IOA concept, the definition of an IOA solution – mechanisms, interfaces, requirements – that can be applied to the identified scenario and perimeter.
The proposed GNC solution aims to be versatile and adaptable to different scenarios. The Navigation can make use of existing and well proven techniques, such as visual-based navigation systems with fiducial markers. The proposed Guidance approach will be concentrated on the use of on-board constrained convex-optimization for trajectory-planning, including a model predictive control (MPC) framework. The Control problem tackles several objectives, taking into account the importance of controller robustness. Thus, well proven robust control techniques will be considered along with modern techniques, for robust stability, performance and sensitivity. The inherent high non-linearity of a robotic manipulator will also be taken into account in the design of the proposed control scheme for the manipulator operations, and combinations of linear and non-linear control schemes will also be studied in this framework. The proposed control techniques will be combined with System Identification capabilities, to enhance GNC performance. These functions need to be managed autonomously, with no continuous Ground intervention, so a highly autonomous Mode Manager is part of the proposed GNC.
Methodology for DDVV
The activity proposes an incremental design based on the chain Model-in-the-Loop (MIL) ➔ Autocoding ➔ Software-in-the-Loop (SIL) ➔ Processor-in-the-Loop (PIL) Testbench ➔ E2E Proof of Concept Testbench.
This approach allows to minimize the risks during the activity. In addition, this chain can provide invaluable support during the Design and Development phases and possibility to test V&V requirements already at early and intermediate design phases, allowing fast design iterations and feedback already at the early design phases and the possibility to correct design problems at those early phases, thus, making more affordable the required effort.
The MIL phase relies on the use of a detailed, multi-physics simulator to captures the main real-world effects and allows to simulate the behaviour of manipulators, contact dynamics, and changes of physical properties. This simulator is used to validate the first version of the developed GNC algorithms in MATLAB/Simulink and associated physical modelling tools.
Through a process of autocoding, these algorithms can be converted into C-code to be embedded for execution in test boards. A SIL and PIL stage ensures that the code generated is equivalent to the original algorithms and that it can be executed into a representative processor.
Finally, a scaled version of the algorithms developed will be used in ground test facilities using scaled-down models, replacing the simulated dynamics by real vehicles and manipulators, whose results can be scaled back and used to derive conclusions on the original, large-scale mission.
