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

机译：述评空间/航空航天车辆的高级指导和控制算法

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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 stateof-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.
机译：用于空间/航空航天车辆的高级指导和控制（G＆C）系统的设计在过去几十年中在全球范围内得到了大量的关注，并将继续成为航空航天行业的主要重点。毫不奇怪，由于存在各种模型的不确定性和环境干扰，基于鲁棒和随机控制的方法在G＆C系统设计中发挥了关键作用，并且已经成功地建造了许多有效的算法以指导和转向空间/航空航天的运动车辆。除了这些稳定的理论型技术外，近年来，我们目睹了设计优化理论的和人工智能（AI）基于空间/航空航天车辆的控制器的日益增长的趋势，以满足对更好系统性能的不断增长的需求。相关研究表明，这些新开发的策略可以从应用程序角度带来许多好处，并且可以考虑驱动车载决策系统。在本文中，我们提供了对现有技术算法的系统调查，该算法能够为空间/航空航天车辆产生可靠的指导和控制命令。本文首先提供了空间/航空航天车辆引导和控制问题的简要概述。在此之后，讨论了关于稳定性理论的基于理论的G＆C方法的广泛集合。审查和讨论了这些方法中固有的一些潜在问题和挑战。然后，给出了概述的最近开发的优化理论的方法，该方法具有产生最佳指导和控制命令，包括基于动态编程的方法，模型预测控制的方法和其他增强版本。还讨论了应用这些方法的关键方面，例如主要优势和固有的挑战。随后，给出了最近尝试探索与车辆系统的最佳控制相关的AI技术可能使用的特定焦点。讨论的亮点说明了空间/航空航天车辆控制问题如何从这些AI模型中受益。最后，一些实际的实施考虑因素与许多未来的研究主题一起进行了总结。

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