Uncertain parameters analysis of powered-descent guidance based on Chebyshev interval method

Cheng, Long; Wen, Hao; Jin, Dongping

doi:10.1016/j.actaastro.2019.05.040

Cited by 12 publications

(1 citation statement)

References 26 publications

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“…This "simultaneous" feedback-feedforward design was done via multidisciplinary optimization in (Jiang et al, 2018), desensitized optimal control in (Shen et al, 2010;Seywald and Seywald, 2019), chance-constrained optimization in (Ono et al, 2015), and covariance steering in Tsiotras, 2018, 2019). Other work in this domain includes open-loop robust trajectory design via Chebyshev interval inclusion (Cheng et al, 2019c), and a posteriori statistical analysis through linear covariance propagation (Woffinden et al, 2019) and Monte Carlo simulation (Scharf et al, 2017). PDG methods based on lossless convexification and SCP are in most cases implicit guidance methods.…”

Section: Powered Descent Guidance For Rocket Landingmentioning

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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