SHEFEX-3 Optimal Feedback Entry Guidance

Sagliano, Marco; Samaan, Malak; Theil, Stephan; Mooij, Erwin

doi:10.2514/6.2014-4208

Cited by 13 publications

(7 citation statements)

References 3 publications

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“…In this paper we focus on SPARTAN, an optimal-control package developed by the German Aerospace Center, which has already been used in literature [7][8][9][10][11][12][13] . SPARTAN uses the flipped version of the RPM: the global Flipped Radau Pseudospectral Method (FRPM), based on the flipped distribution of points w.r.t.…”

Section: Pseudospectral Methodsmentioning

confidence: 99%

Exact Hybrid Jacobian Computation for Optimal Trajectories via Dual Number Theory

D’Onofrio

Sagliano

Arslantas

2016

AIAA Guidance, Navigation, and Control Conference

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In this paper the effects of the use of the dual-based hybrid Jacobian computation in combination with the Pseudospectral Methods are thoroughly inspected. The dual-step differentiation method is implemented in SPARTAN (SHEFEX-3 Pseudospectral Algorithm for Re-entry Trajectory ANalysis), a tool based on the use of the global Flipped Radau Pseudospectral method for the transcription of optimal control problems. The dual number theory is exploited to provide an exact computation of the Jacobian matrix associated with the NonLinear Programming (NLP) problem to be solved. The dual-step differentiation method is compared to standard differentiation schemes (the central difference and the complex-step approximations) and applied in the solution of two examples of optimal control problem using two different off-the-shelf NLP solvers (SNOPT and IPOPT). Differentiation based on dual number theory is proved to be a valid alternative to the traditional, well-known, differentiation schemes as its use improves, for the problems analysed, the accuracy of the results, especially in combination with SNOPT.

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Section: Pseudospectral Methodsmentioning

confidence: 99%

Exact Hybrid Jacobian Computation for Optimal Trajectories via Dual Number Theory

D’Onofrio

Sagliano

Arslantas

2016

AIAA Guidance, Navigation, and Control Conference

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“…3): the envelope of yellow and red curves represents the lower bound (steep entry) and the blue curve the upper bound (shallow entry). The corridor is so narrow that the guidance and control systems are critical for a safe entry [12]. However, being the design of a controller out of the scope of this work, some violation of the constraints is accepted, under certain conditions.…”

Section: A Entry Corridormentioning

confidence: 99%

A Gliding Vehicle for ISS Crew Rescue - Mission Operational Concept

Volo

Pietro²

2018

2018 SpaceOps Conference

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The purpose of this preliminary study is to analyse important operational aspects of using a generic gliding vehicle for ISS Crew Rescue Missions. In particular, mission duration, coast and re-entry windows, ground coverage and maximum loads are investigated. A fourstep approach is followed: first, re-entry corridor is computed taking into account thermomechanical constraints; second, the descent trajectory is simulated down to an altitude of 25 km, where the Terminal Area Energy Management (TAEM) guidance switches on. Third, footprint is computed for every point along the ISS orbit with a computationally-efficient approach to easily define both available re-entry windows and coast phases. Fourth, ground network coverage is analysed for a family of possible trajectories. The results of this preliminary analysis show that, considering a single target airport in Europe, the maximum coasting between two consecutive windows lasts about 6 hours. Moreover, it is demonstrated that the coast duration can be halved by adding an extra-runway in the Indian Ocean. Concerning ground coverage, it is shown that using a conventional network of stations does not provide sufficient link. Therefore, a satellite data relay system, like the American TDRSS, is highly recommended. Apart from number and comfort of the seats (important in case of injured astronauts), the short coasting duration, the possibility of landing on a runway and the low accelerations at re-entry are the main strengths of a gliding vehicle compared to a Soyuz-like capsule. I. Nomenclature= Earth oblateness r = geocentric radius t = time * Mission Analysis Engineer, Systems Engineering Department at AVIO Spa on behalf of AizoOn Consulting, strada del Lionetto, Turin, giuseppe.dicampli@aizoon.it.

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“…The roots of the linear combination of Legendre polynomials are obtained by RPM and flipped in the domain [-1,1]. SPARTAN (SHEFEX-3 Pseudospectral Algorithm for Reentry Trajectory ANalysis), an in-house developed trajectory generation tool, is used for the discretization of the problem [14,15].…”

Section: Optimal Control Problemmentioning

confidence: 99%

Attainable Landing Area Computation of a Lunar Lander with Uncertainty by Reachability Analysis

Arslantas

Theil²

2017

Advances in Aerospace Guidance, Navigation and Control

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Soft landing is one of the most critical phases for space missions which require landing a spacecraft on the surface of a body like asteroids or planets, as well as concepts like reusable launch vehicles. In order to ensure safety and success for the terminal landing phase, in addition to hazard maps obtained by on-board sensors, there is also a need for a map which characterizes the attainable landing area that the lander can achieve by obeying constraints within the presence of uncertainties. This paper proposes a method to obtain the attainable landing area of a lunar lander with uncertainties by reachability analysis. The method obtains the set of achievable states for a dynamical system starting from an initial condition with given admissible control inputs of the system. Nonconvex reachable sets (RS) are computed using optimal control. The candidate landing area on the Moon surface is represented by equidistant grid points and for each point an optimal control problem (OCP) is defined. The corresponding OCP is transcribed into a finite dimensional Nonlinear Programming Problem (NLP) by using Pseudospectral Methods (PSM). The solution of the NLP leads to the RS of the dynamical system. A Riccati equation-based controller is designed to track the reference trajectories. Monte Carlo simulations are carried out to obtain the safely attainable landing area of the lunar lander as probability maps.

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SHEFEX-3 Optimal Feedback Entry Guidance

Cited by 13 publications

References 3 publications

Exact Hybrid Jacobian Computation for Optimal Trajectories via Dual Number Theory

Exact Hybrid Jacobian Computation for Optimal Trajectories via Dual Number Theory

A Gliding Vehicle for ISS Crew Rescue - Mission Operational Concept

Attainable Landing Area Computation of a Lunar Lander with Uncertainty by Reachability Analysis

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