Customized Real-Time Interior-Point Methods for Onboard Powered-Descent Guidance

Dueri, Daniel; Açıkmeşe, Behçet; Scharf, Daniel P.; Harris, Matthew W.

doi:10.2514/1.g001480

Cited by 129 publications

(54 citation statements)

References 30 publications

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“…is a consequence of the mapping between pseudospectral and physical time domains described in Eq. (12) and (13). For the fRPm the weights w i can be computed as…”

Section: Flipped Radau Pseudospectral Methods and Lobatto Pseudospementioning

confidence: 99%

Pseudospectral Convex Optimization for Powered Descent and Landing

Sagliano

2018

Journal of Guidance, Control, and Dynamics

123

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“…is a consequence of the mapping between pseudospectral and physical time domains described in Eq. (12) and (13). For the fRPm the weights w i can be computed as…”

Section: Flipped Radau Pseudospectral Methods and Lobatto Pseudospementioning

confidence: 99%

Pseudospectral Convex Optimization for Powered Descent and Landing

Sagliano

2018

Journal of Guidance, Control, and Dynamics

123

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“…In planetary landing problems, the possibility to generate optimal guidance profiles on-board has been studied and suggested to significantly enhance a vehicles landing accuracy [6,11]. The resulting algorithms need to run on a radiation-hardened flight processor, that is on a significantly slower processor than those available on modern desktops and one having significant architectural differences.…”

Section: On-board Optimal Guidance Via a Deep Networkmentioning

confidence: 99%

Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

Izzo

Sprague

Tailor

2019

Springer Optimization and Its Applications

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After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth-Mars orbital transfer, extend the findings made previously for landing scenarios and quadcopter dynamics, opening a new research area in interplanetary trajectory planning.

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“…To further improve the efficiency of the convex optimization algorithms for potential onboard and embedded applications, it is important to develop customized algorithms for faster computational speed, which have been gained much attention in recent years [59,63,64]. Specifically, customized algorithms for second-order cone programming problems were introduced and successfully used for onboard powered descent guidance in a practical vertical-takeoff and vertical-landing rocket [65,66].…”

Section: Numerical Algorithmsmentioning

confidence: 99%

Survey of convex optimization for aerospace applications

2017

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Convex optimization is a class of mathematical programming problems with polynomial complexity for which state-of-the-art, highly efficient numerical algorithms with predeterminable computational bounds exist. Computational efficiency and tractability in aerospace engineering, especially in guidance, navigation, and control (GN&C), are of paramount importance. With theoretical guarantees on solutions and computational efficiency, convex optimization lends itself as a very appealing tool. Coinciding the strong drive toward autonomous operations of aerospace vehicles, convex optimization has seen rapidly increasing utility in solving aerospace GN&C problems with the potential for onboard real-time applications. This paper attempts to provide an overview on the problems to date in aerospace guidance, path planning, and control where convex optimization has been applied. Various convexification techniques are reviewed that have been used to convexify the originally nonconvex aerospace problems. Discussions on how to ensure the validity of the convexification process are provided. Some related implementation issues will be introduced as well.

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Customized Real-Time Interior-Point Methods for Onboard Powered-Descent Guidance

Cited by 129 publications

References 30 publications

Pseudospectral Convex Optimization for Powered Descent and Landing

Pseudospectral Convex Optimization for Powered Descent and Landing

Machine Learning and Evolutionary Techniques in Interplanetary Trajectory Design

Survey of convex optimization for aerospace applications

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