1,720,968 research outputs found
Covariance control for stochastic low-thrust trajectory optimization
No full textThis 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
Convex optimization of launch vehicle ascent trajectory with heat-flux and splash-down constraints
Full text linkThis 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 Optimization of Ascent and Powered Descent of a Reusable Launch Vehicle
No full textThis paper presents a convex programming approach for the optimization of the full ascent trajectory of reusable launch vehicles, from lift-off to orbit payload injection, together with the soft landing of the first stage. A combination of lossless and successive convexification methods is employed to handle the nonlinear dynamics and constraints. Two strategies for the recovery of the first stage, that is, downrange landing and return-to-launch site, are discussed. Preliminary results are presented to show the effectiveness and performance of the proposed approach for a study case involving a two-stage launch vehicle
Convex approach to three-dimensional launch vehicle ascent trajectory optimization
No full textThis 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
Convex Approach to Covariance Control for Low-Thrust Trajectory Optimization with Mass Uncertainty
No full textThis paper presents a convex approach to the design of optimal space trajectories while explicitly accounting for uncertainty. A covariance control problem aimed at driving a stochastic system from an initial probability distribution to a desired one at a final time is formulated to retrieve an optimal nominal trajectory and an additive state feedback controller that can compensate for exogenous in-flight disturbances. The feedback controller regulates the thrust magnitude and, consequently, the propellant mass consumption. As a result, mass is a random state variable with a significant variance if high disturbances are considered. The propagation of the mass variance is considered in the optimization to retrieve a control policy that is robust to mass uncertainties. Convexification strategies, including changes of variables, lossless relaxations, and successive linearization, are used to obtain a deterministic convex optimization problem solvable with low computational complexity. A numerical example consisting of a low-thrust transfer from the Earth to Mars is considered. Extensive Monte Carlo campaigns are carried out to assess the effectiveness and performance of the attained control policy
Autonomous upper stage guidance with robust splash-down constraint
Full text linkThis 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
Trajectory Reconstruction of Launch Vehicle in Atmospheric Flight using the Unscented Kalman Filter
No full textIn this paper the performance of a smoothed Unscented Kalman filter is analyzed for the determination of the best-estimated trajectory of a launch vehicle in atmospheric flight. A kinematic formulation of the filter is considered, that makes use of vehicle and ground-based measurements, where acceleration and angular velocity measured by an Inertial Measurement Unit are treated as inputs in the filter model, and the states include uncertainty parameters such as IMU biases. Results for state estimation and related uncertainty are presented and discussed for a case study involving the first-stage flight of the ARES I launch vehicle, where synthetic measurement data are generated by a nonlinear high-fidelity dynamic model of the vehicle, and the estimates obtained by the proposed method are compared to those evaluated using the Extended Kalman filter algorithm
Structural design of booms for the solar sail of Helianthus sailcraft
No full textSolar 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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