Reliable spacecraft attitude control depends on accurate prediction of attitude dynamics, particularly when model-based strategies such as Model Predictive Control (MPC) are employed, where performance is limited by the quality of the internal system model. For spacecraft with complex dynamics, obtaining accurate physics-based models can be difficult, time-consuming, or computationally heavy. Learning-based system identification presents a compelling alternative; however, models trained exclusively on data frequently exhibit fragile stability properties and limited extrapolation capability. This work explores Physics-Informed Neural Networks (PINNs) for modeling spacecraft attitude dynamics and contrasts it with a conventional data-driven approach. A comprehensive dataset is generated using high-fidelity numerical simulations, and two learning methodologies are investigated: a purely data-driven pipeline and a physics-regularized approach that incorporates prior knowledge into the optimization process. The results indicate that embedding physical constraints during training leads to substantial improvements in predictive reliability, achieving a 68.17% decrease in mean relative error relative. When deployed within an MPC architecture, the physics-informed models yield superior closed-loop tracking performance and improved robustness to uncertainty. Furthermore, a hybrid control formulation that merges the learned nonlinear dynamics with a nominal linear model enables consistent steady-state convergence and significantly faster response, reducing settling times by 61.52%-76.42% under measurement noise and reaction wheel friction.
Figures & tables
Fig. 1 : Schematic of the proposed hybrid MPC with learned dynamics. When the attitude error is below 1 degree we switch from a nonlinear MPC with PINN state estimator (red) to a linear MPC (yellow).
TABLE I : We show the satellite inertia matrix IS , the RWs inertia matrix Irw , the satellite mass, the maximum torque acting on the RWs urw , the maximum RWs speed ωrw .
Experiment
MRE
Physics Error
MRE self-loop
Physics Error self-loop
MLP-DD
19.67
24.38
22.63
137.34
MLP-LD
6.26
17.18
6.96
95.86
TABLE II : Results obtained when evaluating the MLP models as regressors. MLP-DD uses only the data-driven loss, while MLP-LD uses a Lagrangian dual approach.
Fig. 2 : 300 MC simulation with parameter estimation errors, state estimation noise and RWs friction for MLP-LD (left), traditional MPC with linear (center) and MLP-LD + Linear (right).
Experiment
Steady-State Error [degrees]
Settling Time [s]
MLP-LD
0.2901
47.40
MLP-LD + Linear
0.0023
43.90
Non-Linear MPC
0.1215
186.2
Linear MPC
0.0024
114.1
TABLE III : Results of the experiments performed to study the robustness-to-noise of the PINN model when used in a MPC with several sources of noise. We show the median over 300 MC simulations.
Underactuated spacecraft faces controllability limitations and heightened sensitivity to environmental disturbances, complicating attitude maneuvering and stabilization. Due to the lack of control authority along the underactuated axis, conventional controllers cannot directly stabilize all attitude components and therefore require reference planning strategies. Furthermore, MPC approaches remain sensitive to inertia uncertainty and unmodeled dynamic couplings, resulting in degraded tracking performance under mismatch. To address these issues, we consider a hierarchical architecture integrating three layers: (i) a nonlinear model predictive controller (NMPC) for constraint and underactuation-aware maneuver planning and nominal closed-loop stability under actuator limits; (ii) a physics-informed neural network (PINN) trained offline on simulation data to estimate residual disturbance torques, with loss terms that enforce consistency with rigid-body rotational dynamics; (iii) a Lyapunov-based supervisory safety mechanism that evaluates the learned correction online and bounds or suppresses its influence to preserve the stability properties of the baseline controller. The architecture is evaluated in a high-fidelity simulation environment modelling reaction wheel dynamics, actuator saturation, and environmental disturbances. Monte Carlo studies show statistically significant reductions in steady-state attitude error relative to standalone NMPC while maintaining robust behavior under uncertainty. The supervisory layer ensures graceful degradation to purely model-based control when the learning-based augmentation is unreliable.
Amirhossein Ayanmanesh Motlaghmofrad, Carlo Cena, Mauro Martini +1
Politecnico di Torino, Torino (TO), ITA · Argotec S.R.L., San Mauro (TO), ITA
This work introduces two lightweight model predictive control (MPC) approaches for attitude tracking with reaction wheels during spacecraft rendezvous synchronization. Both approaches are based on a novel attitude deviation formulation, which enables the use of inherently linear constraints on angular velocity. We develop a single-loop and a dual-loop MPC; the latter embeds a stabilizing feedback controller within the inner loop, yielding a linear time-invariant system. Both controllers are implemented with CasADi - including automatic code generation - evaluated across various solvers, and validated within the Basilisk astrodynamics simulation framework. The experimental results demonstrate improved tracking accuracy alongside reductions in computational effort and memory consumption. Finally, embedded delivery to an ARM Cortex-M7 - representative of commercial off-the-shelf devices used in New Space platforms - confirms the real-time feasibility of these approaches and highlights their suitability for onboard attitude control in resource-constrained spacecraft rendezvous missions.
Peter Stadler, Alexander Meinert, Niklas Baldauf +1
Space Applications Group, e:fs TechHub GmbH, 85080 Gaimersheim, Germany · Faculty of Electrical Engineering and Information Technology, Ingolstadt University of Applied Sciences, Germany
Nanosatellites (e.g., CubeSats) have the potential to carry out useful science and exploration activities for outer solar system exploration, but their small size leads to tradeoffs between attitude control and science capabilities. Magnetorquers offer a unique compromise as compact actuators that enable three-axis attitude control, but suffer from a loss of control authority and require full knowledge of the planetary magnetic field. In this work, we present a learning-based adaptive model predictive control (MPC) algorithm that plans rotational trajectories for the satellite while accounting for this loss of control authority. Key to our approach is the inclusion of a learned magnetic field model that is trained in simulation using low-fidelity magnetic field data. Using measurements collected online, our proposed algorithm adapts this learned magnetic field model using a simple last-layer update scheme, allowing the MPC controller to quickly adapt to the true planetary magnetic field model and more effectively plan attitude trajectories. We demonstrate the results of our proposed approach for two use cases, (1) Earth-orbit and (2) Uranus-orbit, and show that our learning-based adaptive MPC algorithm improves controller success for these system simulations by 5% and 61%, respectively.
Patrick Schwartz, Abhishek Cauligi
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.