Review of Advanced Guidance and Control Methods

Over the past few decades, how to design a sophisticated guidance and control the (G &C) system for space and aerospace vehicles has been widely researched, which has increasingly drawn attention from all over the world and will continue to do so. As is known to all, there are various model uncertainties and environmental disturbances in G &C system. Therefore, robust and stochastic control-based methods have unsurprisingly played a key role in the system design. Furthermore, a large number of researchers have proposed and successfully established several algorithms which can effectively guide and steer the motion of space/aerospace vehicles. In addition to these stability theory-focused techniques, a major trend in recent years has been the development of optimisation theory- and artificial intelligence (AI)-based controllers for space and aeronautical vehicles in an effort to address the demand for greater system performance. According to related studies, in terms of practical application, these recently established strategies are more advantageous, and they may be suitable for the onboard decision-making system as well. In this chapter, the latest algorithms were analyzed systemically. The chapter begins with a succinct summary of issues with space/aerospace vehicle guidance and control. The discussion of a wide range of scholarly papers pertaining to G &C approaches based on stability theory follows the summary, which examines and explains the potential inherent problems. Then, a summary of different recently proposed optimisation theory-based methods is provided. These methods are expected to generate the optimal guidance and control commands, such as dynamic programming-based, model predictive control-based methods, and other modified versions. This chapter also covered the discussion of their main benefits and inherent drawbacks, which are important in terms of their applications. We also noticed that the combination of AI techniques and the optimal control of vehicle systems has been a new research direction. Therefore, in the following part, we gave a special focus on the recent effort to discuss the feasibility of its application. The key points of the analysis demonstrate how these AI models may be useful for solving space/aerospace vehicle control issues. Finally, a list of potential future study subjects is provided, along with a few concerns for practical implementation.