Enhanced predictor–corrector Mars entry guidance approach with atmospheric uncertainties

Due to the long-range data communication and complex Mars environment, the Mars lander needs to promote the ability to autonomously adapt uncertain situations ensuring high precision landing in future Mars missions. Based on the analysis of multiple disturbances, this study demonstrates an enhanced predictor–corrector guidance method to deal with the effect of atmospheric uncertainties during the entry phase of the Mars landing. In the proposed method, the predictor–corrector guidance algorithm is designed to autonomously drive the Mars lander to the parachute deployment. Meanwhile, the disturbance observer is designed to onboard estimate the effect of fiercely varying atmospheric uncertainties resulting from rapidly height decreasing. Then, with the estimation of atmospheric uncertainties compensated in the feed-forward channel, the composite guidance method is put forward such that both anti-disturbance and autonomous performance of the Mars lander guidance system are improved. Convergence of the proposed composite method is analysed. Simulations for a Mars lander entry guidance system demonstrates that the proposed method outperforms the baseline method in consideration of the atmospheric uncertainties

Space programs summary no. 37-63, volume 1 for the period 1 March - 30 April 1970. Flight projects

Mariner Mars 1971, Mariner Venus-Mercury 1973 and Viking Orbiter 1975 status report

Review on Entry, Descent, Landing of Rovers on Mars

Humans are trying to explore the solar system and the moon was the first. Since the recent decades the interest has shifted towards so called twin planet of Earth. Martian surface having similar features reminiscent both of impact craters of Moon and the various desert and polar ice caps of Earth. Landers and Rovers are the most effective ways to explore Mars in this existing Technology. This is a company’s Hercules job of taking the rover or Lander to the surface. The most crucial part of this type of missions is entry Descent and landing of the instruments on the surface. This process involves a lot of complicated technologies and accuracy in execution; this is discussed in the forthcoming paper

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

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

Space programs summary no. 37-48, volume 1, for the period September 1 to October 31, 1967. Flight projects

Mariner, and Voyager planetary-interplanetary flight projects, and Surveyor lunar progra