arXiv subjects

Valentin Preda

Publications and source records attributed to Valentin Preda.

9 recordsLinked to original sources

arXiv2026

Data-driven Learning of LPV Surrogate Models of Fuel Sloshing

This paper aims to enhance the efficiency of validation and verification campaigns involving fuel sloshing phenomena. Our first contribution is the development of an open-source, high-fidelity and computationally efficient two-dimensional smoothed-particle hydrodynamics-based fuel sloshing simulator that reproduces the dynamics of a spacecraft with a partially filled tank with liquid propellant. Implemented in Python using Jax, the simulator leverages GPU parallelization and supports automatic differentiation, enabling rapid generation of simulation data and system linearizations for general surrogate modelling purposes. Our second contribution is the demonstration of a practical methodology for constructing surrogate models of fuel sloshing from input--output data generated by the simulator, targeting rapid simulation and model-based control applications. The surrogate model employs a Linear Parameter-Varying (LPV) state-space structure with affine dependence on the scheduling variables, providing an accurate yet computationally efficient approximation of the sloshing dynamics. The capabilities of the proposed approach are demonstrated through closed-loop simulations of a rigid spacecraft with a partially filled fuel tank for two manoeuvre profiles under zero-gravity conditions. The identified surrogate enables simulations that are two orders of magnitude faster than the high-fidelity model.

eess.SY↗

arXiv2026

Learning Surrogate LPV State-Space Models with Uncertainty Quantification

The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, facilitating efficient stability and performance analysis together with controller design. Despite significant advances in data-driven LPV modelling, existing approaches do not quantify the uncertainty of the obtained LPV models. Consequently, assessing model reliability for analysis and control or detecting operation outside the training regime requires extensive validation and user expertise. This paper proposes a Bayesian approach for the joint estimation of LPV state-space models together with their scheduling, providing a characterization of model uncertainty and confidence bounds on the predicted model response directly from input-output data. Both aleatoric uncertainty due to measurement noise and epistemic uncertainty arising from limited training data and structural bias are considered. The resulting model preserves the LPV structure required for controller synthesis while enabling computationally efficient simulation and uncertainty propagation. The approach is demonstrated on the surrogate modelling of a two-dimensional nonlinear interconnection of mass-spring-damper systems.

eess.SY↗

arXiv2024

Dynamics modelling and path optimization for the on-orbit assembly of large flexible structures using a multi-arm robot

This paper presents a comprehensive methodology for modeling an on-orbit assembly mission scenario of a large flexible structure using a multi-arm robot. This methodology accounts for significant changes in inertia and flexibility throughout the mission, addressing the problem of coupling dynamics between the robot and the evolving flexible structure during the assembly phase. A three-legged walking robot is responsible for building the structure, with its primary goal being to walk stably on the flexible structure while picking up, carrying and assembling substructure components. To accurately capture the dynamics and interactions of all subsystems in the assembly scenario, various linear fractional representations (LFR) are developed, considering the changing geometrical configuration of the multi-arm robot, the varying flexible dynamics and uncertainties. A path optimization algorithm is proposed for the multi-arm robot, capable of selecting trajectories based on various cost functions related to different performance and stability metrics. The obtained results demonstrate the effectiveness of the proposed modeling methodology and path optimization algorithm.

eess.SY↗

arXiv2022

Modeling, robust control synthesis and worst-case analysis for an on-orbit servicing mission with large flexible spacecraft

This paper outlines a complete methodology for modeling an on-orbit servicing mission scenario and designing a feedback control system for the attitude dynamics that is guaranteed to robustly meet pointing requirements, despite model uncertainties as well as large inertia and flexibility changes throughout the mission scenario. A model of the uncertain plant was derived, which fully captures the dynamics and couplings between all subsystems as well as the decoupled/coupled configurations of the chaser/target system in a single linear fractional representation (LFR). In addition, a new approach is proposed to model and analyze a closed-loop kinematic chain formed by the chaser and the target spacecraft through the chaser's robotic arm, which uses two local spring-damper systems with uncertain damping and stiffness. This approach offers the possibility to model the dynamical behaviour of a docking mechanism with dynamic stiffness and damping. The controller was designed by taking into account all the interactions between subsystems and uncertainties as well as the time-varying and coupled flexible dynamics. Lastly, the robust stability and worst-case performances were assessed by means of a structured singular value analysis.

eess.SY↗

arXiv2022

LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal.

eess.SY↗

arXiv2022

Reconfiguration of a satellite constellation in circular formation orbit with decentralized model predictive control

Satellite constellation missions, consisting of a large number of spacecraft, are increasingly being launched or planned. Such missions require novel control approaches, in particular for what concerns orbital phasing maneuvers. In this context, we consider the problem of reconfiguration of a satellite constellation in a circular formation. In our scenario, a formation of equally spaced spacecraft need to undergo an autonomous reconfiguration due to the deorbiting of a satellite in the formation. The remaining spacecraft have to reconfigure to form again an equidistant formation. To achieve this goal, we consider two decentralized strategies that rely on different sets of information about the neighboring spacecraft in the formation. In the fully decentralized case, each controller knows only the current states of each spacecraft, i.e. position and velocity, while in the second decentralized strategy with with information sharing, the entire planned nominal trajectory of each spacecraft is available to its neighbors. Our numerical simulation results show that, by increasing the amount of information available to each spacecraft, faster reconfiguration maneuvers with smaller fuel consumption can be achieved.

eess.SY↗

arXiv2021

Optimal Science-time Reorientation Policy for the Comet Interceptor Flyby via Sequential Convex Programming

This paper introduces an algorithm to perform optimal reorientation of a spacecraft during a high speed flyby mission that maximizes the time a certain target is kept within the field of view of scientific instruments. The method directly handles the nonlinear dynamics of the spacecraft, sun exclusion constraint, torque and momentum limits on the reaction wheels as well as potential faults in these actuators. A sequential convex programming approach was used to reformulate non-convex pointing objectives and other constraints in terms of a series of novel convex cardinality minimization problems. These subproblems were then efficiently solved even on limited hardware resources using convex programming solvers implementing second-order conic constraints. The proposed method was applied to a scenario that involved maximizing the science time for the upcoming Comet Interceptor flyby mission developed by the European Space Agency. Extensive simulation results demonstrate the capability of the approach to generate viable trajectories even in the presence of reaction wheel failures or prior dust particle impacts.

eess.SY↗

arXiv2021

Integrated modelling of microvibrations induced by Solar Array Drive Mechanism for worst-case end-to-end analysis and robust disturbance estimation

Modern and future observation Space missions face increasingly more demanding pointing performance requirements. This is accompanied with the development of larger lightweight flexible structures. This paper outlines a methodology for modeling a generic multi-body flexible spacecraft in an end-to-end fashion within the Two-Inputs Two-Outputs Port framework: from disturbance all the way to pointing performance by tacking into account all the uncertainties of each sub-system. A particular focus is dedicated to a novel generic model for Solar Drive Array Mechanisms and its harmonic disturbances produced by the micro-stepping driver and the gearbox imperfections. The proposed model is validated with the on-board telemetries of a European spacecraft. Moreover this paper shows how the proposed model-based approach can be easily used for control design and closed-loop robust performance analysis. Worst-case scenarios of mechanical parameters combination are investigated to provide robust performance certificate of the reference study case. A Linear Parameter-Varying observer, scheduled by the solar array rotor angle, is finally proposed in order to estimate the disturbance torques induced by the gearbox imperfections just using the measurements coming from a classical attitude control system: a star tracker and a gyrometer.

eess.SY↗

arXiv2021

Autonomous parafoil precision landing using convex real-time optimized guidance and control

To overcome the limitations of current parafoil precision landing capabilities, an efficient real-time convex optimized guidance and control strategy is presented. Successive convexification of the parafoil guidance problem guarantees local optimality with polynomial convergence rate for efficient real-time implementation, where each iteration is dynamically feasible. Our approach shows reliable and fast numerical convergence through in-flight recalculation of time of flight and a new optimal trajectory to cope with time-varying dynamics. The efficiency of our strategy is demonstrated via a comparative analysis of the existing X-38 in-flight demonstrated guidance and control system. Exhaustive Monte-Carlo simulations show performance improvements of about one order of magnitude. The concept proposed is simple, yet general, as it scales to any atmospheric parafoil landing system and allows efficient implementation relying only on the turn rate information.

eess.SY↗