Autonomous Upper Stage Guidance with Robust Splash-Down Constraint

This paper presents a novel algorithm, based on model predictive control (MPC), for the optimal guidance of a launch vehicle upper stage. The proposed strategy not only maximizes the performance of the vehicle and its robustness to external disturbances, but also robustly enforces the splash-down constraint. Indeed, uncertainty on the engine performance, and in particular on the burn time, could lead to a large footprint of possible impact points, which may pose a concern if the reentry points are close to inhabited regions. Thus, the proposed guidance strategy incorporates a neutral axis maneuver (NAM) that minimizes the sensitivity of the impact point to uncertain engine performance. Unlike traditional methods to design a NAM, which are particularly burdensome and require long validation and verification tasks, the presented MPC algorithm autonomously determines the neutral axis direction by repeatedly solving an optimal control problem (OCP) with two return phases, a nominal and a perturbed one, constrained to the same splash-down point. The OCP is transcribed as a sequence of convex problems that quickly converges to the optimal solution, thus allowing for high MPC update frequencies. Numerical results assess the robustness and performance of the proposed algorithm via extensive Monte Carlo campaigns.Comment: arXiv admin note: text overlap with arXiv:2210.1461

Stochastic Control of Launch Vehicle Upper Stage with Minimum-Variance Splash-Down

This paper presents a novel synthesis method for designing an optimal and robust guidance law for a non-throttleable upper stage of a launch vehicle, using a convex approach. In the unperturbed scenario, a combination of lossless and successive convexification techniques is employed to formulate the guidance problem as a sequence of convex problems that yields the optimal trajectory, to be used as a reference for the design of a feedback controller, with little computational effort. Then, based on the reference state and control, a stochastic optimal control problem is defined to find a closed-loop control law that rejects random in-flight disturbance. The control is parameterized as a multiplicative feedback law; thus, only the control direction is regulated, while the magnitude corresponds to the nominal one, enabling its use for solid rocket motors. The objective of the optimization is to minimize the splash-down dispersion to ensure that the spent stage falls as close as possible to the nominal point. Thanks to an original convexification strategy, the stochastic optimal control problem can be solved in polynomial time since it reduces to a semidefinite programming problem. Numerical results assess the robustness of the stochastic controller and compare its performance with a model predictive control algorithm via extensive Monte Carlo campaigns

Convex Optimization of Launch Vehicle Ascent Trajectory with Heat-Flux and Splash-Down Constraints

This paper presents a convex programming approach to the optimization of a multistage launch vehicle ascent trajectory, from the liftoff to the payload injection into the target orbit, taking into account multiple nonconvex constraints, such as the maximum heat flux after fairing jettisoning and the splash-down of the burned-out stages. Lossless and successive convexification are employed to convert the problem into a sequence of convex subproblems. Virtual controls and buffer zones are included to ensure the recursive feasibility of the process and a state-of-the-art method for updating the reference solution is implemented to filter out undesired phenomena that may hinder convergence. A hp pseudospectral discretization scheme is used to accurately capture the complex ascent and return dynamics with a limited computational effort. The convergence properties, computational efficiency, and robustness of the algorithm are discussed on the basis of numerical results. The ascent of the VEGA launch vehicle toward a polar orbit is used as case study to discuss the interaction between the heat flux and splash-down constraints. Finally, a sensitivity analysis of the launch vehicle carrying capacity to different splash-down locations is presented.Comment: 2020 AAS/AIAA Astrodynamics Specialist Virtual Lake Tahoe Conferenc

LICIACube on DART Mission: An Asteroid Impact Captured by Italian Small Satellite Technology

In the frame of the Planetary Defense program, NASA developed the Double Asteroid Redirection Test (DART) mission and the Italian Space Agency joined the effort. DART’s spacecraft will act as a kinetic impactor by deliberately crashing into the moonlet of Didymos binary system (i.e. Didymos-B) while the effects of the impact will be observed by a small satellite, the Light Italian CubeSat for Imaging of Asteroid (LICIACube) and ground-based telescopes. LICIACube, an Italian Space Agency (ASI) mission, will fly with a relative velocity of approximately 6.5 km/s and it will document the effects of the impact, the crater and the evolution of the plume generated by the collision. LICIACube will have to maintain the asteroid\u27s pointing at an angular speed of approximately 10 deg/s to fly-by the asteroid close to the Didymos-B surface. The images acquired by LICIACube will be processed onboard through the autonomous navigation algorithm to identify the asteroid system and control the satellite attitude. They will also help the scientific community and provide feedback to the Planetary Defense program, pioneered by the Space Agencies. This deep-space mission is based on a small scale but highly technological platform, whose development is involving both the Italian technical and scientific community

Covariance Control for Stochastic Low-Thrust Trajectory Optimization

This paper outlines a novel approach to the design of optimal space trajectories under significant uncertainty. Finite-horizon covariance control, i.e., the steering of a system from an initial probability distribution to a desired one at a prescribed time, is employed to plan an optimal nominal path along with a robust feedback controller that compensates for exogenous in-flight disturbances. The major contribution of the present paper is a mindful convexification strategy to recast the nonlinear covariance control problem as a deterministic convex optimization problem. The convexification is based on a convenient change of variables that allows to relax the covariance matrix discrete-time propagation into a set of semidefinite cone constraints. While featuring a larger feasible space, the relaxed problem shares the same optimal solution as the original one, as proven by numerical experiments, hence demonstrating that the proposed relaxation is lossless. Monte Carlo campaigns are carried out to validate the in-flight performance of the attained control policies

Autonomous Upper Stage Guidance with Robust Splash-Down Constraint

This paper presents a novel algorithm, based on model predictive control (MPC), for the optimal guidance of a launch vehicle upper stage. The proposed strategy not only maximizes the performance of the vehicle and its robustness to external disturbances, but also robustly enforces the splash-down constraint. Indeed, uncertainty on the engine performance, and in particular on the burn time, could lead to a large footprint of possible impact points, which may pose a concern if the reentry points are close to inhabited regions. Thus, the proposed guidance strategy incorporates a neutral axis maneuver (NAM) that minimizes the sensitivity of the impact point to uncertain engine performance. Unlike traditional methods to design a NAM, which are particularly burdensome and require long validation and verification tasks, the presented MPC algorithm autonomously determines the neutral axis direction by repeatedly solving an optimal control problem (OCP) with two return phases, a nominal and a perturbed one, constrained to the same splash-down point. The OCP is transcribed as a sequence of convex problems that quickly converges to the optimal solution, thus allowing for high MPC update frequencies. Numerical results assess the robustness and performance of the proposed algorithm via extensive Monte Carlo campaigns

Convex optimization of launch vehicle ascent trajectory with heat-flux and splash-down constraints

This paper presents a convex programming approach to the optimization of a multistage launch vehicle ascent trajectory, from the liftoff to the payload injection into the target orbit, taking into account multiple nonconvex constraints, such as the maximum heat flux after fairing jettisoning and the splash-down of the burned-out stages. Lossless and successive convexification methods are employed to convert the problem into a sequence of convex subproblems. Virtual controls and buffer zones are included to ensure the recursive feasibility of the process, and a state-of-the-art method for updating the reference solution is implemented to filter out undesired phenomena that may hinder convergence. A hp pseudospectral discretization scheme is used to accurately capture the complex ascent and return dynamics with a limited computational effort. The convergence properties, computational efficiency, and robustness of the algorithm are discussed on the basis of numerical results. The ascent of a VEGA-like launch vehicle toward a polar orbit is used as a case study to discuss the interaction between the heat flux and splash-down constraints. Finally, a sensitivity analysis of the launch vehicle carrying capacity to different splash-down locations is presented

Convex approach to three-dimensional launch vehicle ascent trajectory optimization

This paper deals with the optimization of the ascent trajectory of a multistage launch vehicle, from liftoff to the payload injection into the target orbit, considering inverse-square gravity acceleration and aerodynamic forces. A combination of lossless and successive convexification techniques is adopted to generate a sequence of convex problems that rapidly converges to the original problem solution. An automatic initialization strategy is proposed to make the solution process completely autonomous. In particular, a novel three-step continuation procedure is developed and proved to be more efficient than simpler strategies. This approach relies on the solution of intermediate problems, which either neglect atmospheric drag or fix the time-lengths of the launch vehicle ascent phases, that are solved in succession, gradually passing from easier instances of the optimization problem to the originally intended problem. State-of-the-art techniques to deal with such a complex problem are adopted to enhance the convergence rate, including safeguarding modifications, such as virtual controls and an adaptive trust region. To assess the validity of the proposed approach in a practical scenario, numerical results are presented for two representative practical applications, using as reference a Falcon 9 launch vehicle

High-performance polyimide membranes for use in solar sail propulsion

Solar sailing is a propulsion technology that is capable of driving vehicles and artificial satellites in space without the use of chemical propellants or electrical systems. This propulsion utilizes the solar radiation pressure resulting from the momentum transfer of solar photons reflected off the sail membrane, which is made of an aluminum-coated thin polymer film. The choice of the polymeric material to be used as solar sail membrane is crucial aspect as it influences the correct deployment of the structure and its efficacy as propulsion system during the space mission. In this work, we synthesized several types of polyimides with aromatic chemical structure using different organic solvents, including a greener alternative to traditional toxic solvents such as dimethylacetamide, and tested their properties for potential use in solar sailing. Thin polyimide membranes with thickness below 3 μm were fabricated and their chemical and physical properties investigated using several experimental techniques, from infrared spectroscopy to calorimetry and water contact angle analysis. Results were used to assess the potential use of the in-house-made polyimide membranes for the Helianthus mission, a test study case of the research program on Solar Photonic Propulsion, which is under development jointly between Sapienza University of Rome and the Italian Space Agency (ASI)

Structural design of booms for the solar sail of Helianthus sailcraft

Solar sail is a promising propulsion concept that exploits solar pressure to navigate in space without the use of propellants, therefore enabling missions otherwise not attainable by traditional propulsion (i.e. electric or chemical propulsion). For instance, a synchronous solar sail with the Earth-Moon barycenter to be used as a long warning time of solar storms caused by Coronal Mass Ejections is the main objective of the Helianthus project, funded by the Italian Space Agency. This paper is aimed specifically at the presentation and description of the design of the structural subsystem for the solar sail of the Helianthus project. This subsystem is composed of four deployable ultralight booms, which deploy and keep the sail-membrane in tension. The booms have to withstand the axial load, generated by the tensioned membrane, which must be smaller than the critical load at buckling. At the same time, the booms need to have sufficient stiffness to prevent a large out-of-plane displacement of the membrane leading to reduction of the thrust. First, the geometry and the dimensions of the boom cross-section to optimize the stiffness is determined. Then, a structural numerical analysis on a full-scale model of a square solar sail (40 m x 40 m) with four supporting booms is performed. For such configuration, the sail tension is simulated in order to determine the axial load acting on the tip of each boom and the displacements due to the solar radiation pressure are evaluated. Simulations are carried out by finite element method using the software ABAQUS. Results are presented at both system and individual components level