Search arXivSearch

arXiv · 2309.16589

Connecting Space Missions Through NGSO Constellations: Feasibility Study

Abstract

A satellite internet provider (e.g., Starlink, OneWeb, O3b mPOWER), despite possessing the capability to provide internet services to on-ground users in a global scale, can dramatically change the way space missions are designed and operated in the foreseeable future. Assuming a scenario where space mission satellites can access the internet via a space internet system, the satellite can be connected to the network permanently (24x7) and act as mere terminal independently from its location. The ability to communicate with the satellite on-demand has the potential to improve aspects such as real-time tasking, outage minimization, operation cost, and dependency on the ground. This paper performs a feasibility study on the concept of connecting space missions to the network through commercial mega-constellations. This study includes a review of existing and near-future space internet systems, identification of candidate space missions for the aforementioned concept, a necessary adaptation of existing Commercial off-the-shelf (COTS) terminals to be plugged into space mission satellites, assessment of communication performance, and investigation of the legal aspects of the radio frequency (RF) spectrum usage. The paper evidences that the concept is practically possible to implement in the near future. Among the studied space internet systems (i.e. Starlink, OneWeb, O3b mPOWER), O3b mPOWER stands out as the most suitable system allowing permanent coverage of low earth orbit (LEO) space missions with data rates that can reach up to 21 Mbps per satellite. Although the concept is very promising and can be implemented in the near future, our investigations show that some regulatory aspects regarding the RF usage should be solved for future exploitation of connecting space missions through NGSO constellations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Houcine Chougrani, Oltjon Kodheli, Ali Georganaki, Jan Thoemel, Chiara Vittoria Turtoro, Frank Zeppenfeldt, Petros Pissias, Mahulena Hofmann, Symeon Chatzinotas. 2024-02-27. Connecting Space Missions Through NGSO Constellations: Feasibility Study. https://doi.org/10.3389/frcmn.2024.1356484

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions

This paper presents a two-stage framework for constrained feedback control of input-affine nonlinear systems. Offline, an approximate value function for the unconstrained problem is computed, for example using Hamilton--Jacobi--Bellman (HJB)-based policy iteration. Online, the proposed quadratic program (QP) minimizes the pre-Hamiltonian evaluated using the approximate value-function gradient subject to safety constraints enforced by control barrier functions (CBFs). This architecture decouples performance optimization from constraint enforcement, allowing constraints to be modified without recomputing the value function. As in CBF-QP architectures based on control Lyapunov functions (CLFs), safety is enforced as a hard constraint; however, the performance objective targets approximate optimality rather than a prescribed Lyapunov decay. Numerical results on a linear 2-state hovercraft and a nonlinear 9-state spacecraft attitude-control problem show agreement with the constrained open-loop optimal control problem (OCP) benchmark in the linear case, and performance close to the OCP benchmark, improving on CLF-based controllers, in the nonlinear case.

eess.SY

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters, including the measurement-noise covariance, are approximated using particle-based Stein Variational Gradient Descent (SVGD), enabling stable real-time inference. To handle parameters subject to physical constraints, we further introduce constrained variants that enforce them through an alternating direction method of multipliers (ADMM) splitting of the SVGD update, including nonlinear equality constraints that standard particle filters cannot readily handle. We derive a stability bound that relates the approximation error in the parameter posterior to the resulting error in the marginal state distribution. Performance of the proposed filters is validated on three case studies: a fed-batch bioreactor with Haldane kinetics and a damped pendulum, both under physical constraints, and a neural-network-augmented dynamic system. The examples cover parameter estimation under inequality and equality constraints and online neural-network training within a dynamical model.

eess.SY

Firing Rate Neural Network Implementations of Model Predictive Control

Human and animal brains perform planning to enable complex movements and behaviors, a process that can be effectively described using model predictive control (MPC). How could the brain physically implement MPC? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. We propose a constructive method; no training is required. This is done first applying the projected gradient method to the dual problem to derive a baseline neural network implementation. We then use factorization and contraction analysis to systematically generate alternative network architectures; in other words, we systematically generate hypotheses for how planning is done in the brain via neural dynamics. Finally, we present numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand), including one example in which imposing sparse connectivity (a property observed in brain networks) does not degrade control performance.

eess.SY