The 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.

在过去的几十年中，用于太空/航空航天器的高级制导与控制（G＆C）系统的设计在全世界引起了广泛关注，并将继续成为航空航天工业的主要重点。毫不奇怪，由于存在各种模型不确定性和环境干扰，基于健壮和随机控制的方法已在G＆C系统设计中发挥了关键作用，并且已成功构建了许多有效的算法来指导和操纵空间/航空航天的运动汽车。除了这些面向稳定性理论的技术外，近年来，我们还见证了为太空/航天飞行器设计基于优化理论和基于人工智能（AI）的控制器的趋势，以满足日益增长的对更好的系统性能的需求。相关研究表明，这些新开发的策略从应用程序的角度来看可以带来很多好处，并且可以考虑将其用于驱动车载决策系统。在本文中，我们对最先进的算法进行了系统的综述，这些算法能够为太空/航空航天器生成可靠的制导和控制命令。本文首先简要概述了空间/航空航天器的制导和控制问题。随后，讨论了有关基于稳定性理论的G＆C方法的大量学术著作。这些方法固有的一些潜在问题和挑战将进行审查和讨论。然后，概述了各种最近开发的基于优化理论的方法，这些方法能够产生最佳的制导和控制命令，包括基于动态编程的方法，基于模型预测控制的方法以及其他增强版本。还讨论了应用这些方法的关键方面，例如它们的主要优点和固有挑战。随后，特别关注最近的尝试，以探索与车辆系统的最佳控制相关的AI技术的可能用途。讨论的重点说明了如何从这些AI模型中受益于太空/航空航天器控制问题。最后，总结了一些实际的实现注意事项，以及一些未来的研究主题。也进行了讨论。随后，特别关注最近的尝试，以探索与车辆系统的最佳控制相关的AI技术的可能用途。讨论的重点说明了如何从这些AI模型中受益于太空/航空航天器控制问题。最后，总结了一些实际的实现注意事项，以及许多未来的研究主题。也进行了讨论。随后，特别关注最近的尝试，以探索与车辆系统的最佳控制相关的AI技术的可能用途。讨论的重点说明了如何从这些AI模型中受益于太空/航空航天器控制问题。最后，总结了一些实际的实现注意事项，以及许多未来的研究主题。