Review of advanced guidance and control algorithms for space/aerospace vehicles

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dc.contributor.authorChai, Runqi
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorSavvaris, Al
dc.contributor.authorChai, Senchun
dc.contributor.authorXia, Yuanqing
dc.contributor.authorChen, C.L. Philip
dc.date.accessioned2021-03-09T15:17:16Z
dc.date.available2021-03-09T15:17:16Z
dc.date.issued2021-03-01
dc.description.abstractThe design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised.en_UK
dc.identifier.citationChai R, Tsourdos A, Savvaris A, et al., (2021) Review of advanced guidance and control algorithms for space/aerospace vehicles. Progress in Aerospace Sciences, Volume 122, April 2021, Article number 100696en_UK
dc.identifier.issn0376-0421
dc.identifier.urihttps://doi.org/10.1016/j.paerosci.2021.100696
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16458
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArtificial intelligenceen_UK
dc.subjectOptimisation theoryen_UK
dc.subjectStability theoryen_UK
dc.subjectSpace/aerospace vehiclesen_UK
dc.subjectGuidance and controlen_UK
dc.titleReview of advanced guidance and control algorithms for space/aerospace vehiclesen_UK
dc.typeArticleen_UK

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