Sequential Convex Programming for Optimal Line of Sight Steering in Agile Missions
Creators: name:  L. HEWING 
name:   J. RAMIREZ 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: FLIGHT DYNAMICS
GNC
AVIONICS
EQUIPMENT
IMAGE PROCESSING

description: Within the Earth observation market, there is a growing demand for ever-larger amounts of high-quality observation data [4]. One way to improve image quality is to enhance the performance of the optical payloads, be it through improvements at the payload level itself, or at AOCS levels via improved pointing stability. Increasing the quantity of available information, on the other hand, requires increased access rates to geographical locations of interest, as well as enhanced coverage and revisit times, thus calling for agile manoeuvres. Together with the current trend towards on-board autonomy and spacecraft miniaturization, this presents significant potential for advances in efficient Line of Sight (LoS) steering by making optimal use of the limited resources available. Performing these agile manoeuvres means exploiting the limits of the spacecraft dynamic capabilities while ensuring a safe motion, e.g., avoiding flexible modes, maintaining a minimum angle between the Sun and the Solar panels, or avoiding certain pointing directions with the sensors. In this scenario, traditional solutions can remain conservative, for instance, due to the pointing constraints or the need to simplify and overapproximate safety margins. This has increased relevance under the use of control moment gyroscopes with complex momentum envelopes, which are increasingly employed in the field of agile missions. Furthermore, classical slew manoeuvres planning is a hand-crafted process that usually implies the knowledge of the manoeuvres to be performed by the spacecraft, hindering increases in the autonomy of the system. In this contribution, we propose the use of embedded numerical optimal control for attitude guidance in interaction with a target scheduler. This way, we maximize observation profit by optimally exploiting the torque resources of the spacecraft while minimizing the time between targets. This results in a challenging constrained nonlinear optimization problem that, thanks to the advances in computational resources and convex optimization, becomes increasingly attainable for onboard implementation. We focus on the nonlinear optimal control problem for determining the minimum time manoeuvre between two target acquisitions, and deal with it using a shooting approach with sequential convex programming algorithms powered by an interior point solver, both developed in-house at SENER Aeroespacial. As a demonstrator benchmark, we make use of a small Earth observation spacecraft that is to be reconfigured to point to a new target. Depending on the specific formulation at hand, we employ a lossless convexification of potential pointing constraints [2,1], express actuator constraints via polytopes or ellipsoids in order to obtain the overall solution by solving a sequence of quadratically-constrained quadratic programs. We evaluate different manoeuvres and scenarios, including initial orbit misalignment, residual momentum in control devices, and failure cases while comparing to traditional solution approaches. Finally, we evaluate the computational overhead associated with the optimization-based solution, comparing it with available open-source tools [3] and demonstrate the feasibility for onboard implementation. Our results show that using embedded optimization can reliably achieve significant improvements over traditional baselines with limited computational overhead and that we can do so for different scenarios and manoeuvres by investing little effort in tuning the algorithms, making embedded optimization a highly promising technology for future agile missions. References [1] Yoonsoo Kim, Mehran Mesbahi, Gurkirpal Singh and Fred Hadaegh. \"On the Constrained Attitude Control Problem,\" AIAA Guidance, Navigation, and Control Conference and Exhibit, 2004. [2] Danylo Malyuta, Taylor Reynolds, Michael Szmuk, Thomas Lew, Riccardo Bonalli, Marco Pavone, and Behcet Acikmese, Convex optimization for trajectory generation, arXiv preprint, 2021. [3] Robin Verschueren, Gianluca Frison, Dimitris Kouzoupis, Jonathan Frey, Niels van Duijkeren, Andrea Zanelli, Branimir Novoselnik, Thivaharan Albin, Rien Quirynen, and Moritz Diehl, ACADOS: a modular open-source framework for fast embedded optimal control, Mathematical Programming Computation, 2021. [4] Xinwei Wang, Guohua Wu, Lining Xing, and Witold Pedrycz. Agile earth observation satellite scheduling over 20 years: Formulations, methods, and future directions, IEEE Systems Journal, 2021.