Pterodactyl: Development and Performance of Guidance Algorithms for a Mechanically Deployed Entry Vehicle

Johnson, Breanna J.; Rocca-Bejar, Daniela; Lu, Ping; Nikaido, Ben; Hays, Zane B.; D'Souza, Sarah; Sostaric, Ronald R.

doi:10.2514/6.2020-1011

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…The LSTM architecture would continuously update estimates of the density and wind velocity to be expected at future points along the descent from sequences of past features, in a way similar to a nonlinear filter. Such an approach should be compared to state of the art methods for the online estimation of the atmospheric density profile [30,31] or of scaling factors for the aerodynamic forces [32]. The LSTM architecture could also be extended to update estimates of the aerodynamic coefficients, and of thrust characteristics during powered descent.…”

Section: Discussion and Extension Of Resultsmentioning

confidence: 99%

Deep Learning Method for Martian Atmosphere Reconstruction

Amato

McMahon

2021

Journal of Aerospace Information Systems

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The reconstruction of atmospheric properties encountered during Mars entry trajectories is a crucial element of post-flight mission analysis. We propose a deep learning architecture using a Long Short-Term Memory Network (LSTM) for the reconstruction of Martian density and wind profiles from inertial measurements and guidance commands. The LSTM is trained on a large set of Mars entry trajectories controlled through the Fully Numerical Predictor-corrector Entry Guidance (FNPEG) algorithm, with density and wind from the Mars Global Reference Atmospheric Model (GRAM) 2010. We examine the training of the network, ensuring that the LSTM generalizes well to samples not present in the training set, and assess the performance of the network on a separate training set. The errors of the reconstructed density and wind

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Section: Discussion and Extension Of Resultsmentioning

confidence: 99%

Deep Learning Method for Martian Atmosphere Reconstruction

Amato

McMahon

2021

Journal of Aerospace Information Systems

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“…A large body of work is available on predictor-corrector methods for entry guidance (Xue and Lu, 2010;Johnson et al, 2020Johnson et al, , 2018Johnson et al, , 2017Lu, 2014) and for aerocapture (Lu et al, 2015). These methods are based on root-finding algorithms, or variations thereof, and some versions are grounded in solving the necessary conditions of optimality (Lu, 2018).…”

Section: Atmospheric Entrymentioning

confidence: 99%

Advances in Trajectory Optimization for Space Vehicle Control

Malyuta¹,

Yu²,

Elango³

et al. 2021

Preprint

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Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.

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“…The motion of an entry vehicle in atmospheric flight around a spherical, rotating planet is described in planet-relative coordinates by the system of equations [18,19]…”

Section: Entry As a Stochastic Processmentioning

confidence: 99%

Stochastic Entry Guidance

Ridderhof¹,

Tsiotras²,

Johnson³

2021

Preprint

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In this paper, closed-loop entry guidance in a randomly perturbed atmosphere, using bank angle control, is posed as a stochastic optimal control problem. The entry trajectory, as well as the closed-loop controls, are both modeled as random processes with statistics determined by the entry dynamics, the entry guidance, and the probabilistic structure of altitude-dependent atmospheric density variations. The entry guidance, which is parameterized as a sequence of linear feedback gains, is designed to steer the probability distribution of the entry trajectories while satisfying bounds on the allowable control inputs and on the maximum allowable state errors. Numerical simulations of a Mars entry scenario demonstrate improved range targeting performance when using the developed stochastic guidance scheme as compared to the existing Apollo final phase algorithm.

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Pterodactyl: Development and Performance of Guidance Algorithms for a Mechanically Deployed Entry Vehicle

Cited by 12 publications

References 7 publications

Deep Learning Method for Martian Atmosphere Reconstruction

Deep Learning Method for Martian Atmosphere Reconstruction

Advances in Trajectory Optimization for Space Vehicle Control

Stochastic Entry Guidance

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