Planning And Scheduling Spacecraft Observations Under Uncertainties In Dynamic Environment

With the increasing frequency of space exploration, spacecraft observation scheduling has become one of the research focuses in academic and engineering field. Solving this problem could raise the income, reduce the spacecraft running cost or working intensity. It is an indispensable technique for future space activities. However, the main works in this domain are limited within static environment, and lack of relative research and analysis in dynamic environment. With a detailed analysis of the background of spacecraft observation, this dissertation first establishes a reasonable model of spacecraft observation problem. Then, aiming at solving the problem of spacecraft observation rescheduling with uncertainty in dynamic environment, this dissertation systematically studies this problem by analyzing and evaluating the uncertainties, designing the rescheduling policies, generative rescheduling algorithm and repair-based rescheduling algorithm. To be more specific, the main work are as follows:First, trying to establish a model of spacecraft observation problem. Targeting at the deficiencies in the existing modeling work, such as insufficient consideration about constraints, this dissertation establishes a reasonable model of spacecraft observation problem by bringing constraints in space activities into full consideration and simplifying them moderately in hope of laying some foundations for later work.Second, analyzing the uncertainties and evaluating the disturbance degree. After analyzing the uncertainties in spacecraft observation scheduling, this dissertation points out that gradual uncertainties have a fuzzy feature (named II type gradual uncertainties in the present dissertation). So it is difficult to evaluate the disturbance degree of these uncertainties. Aiming at solving this problem, this dissertation designs an algorithm based on fuzzy neural network (FNN) to evaluate the disturbance degree of these uncertainties which lays some foundation for further studies.Third, presenting a policy for spacecraft rescheduling. On the foundation of the above work, in order to effectively manage the rescheduling operations, this dissertation proposes an improved hybrid rescheduling strategy based on FNN. It first evaluates the disturbance degree of II type gradual uncertainties and decides the corresponding rescheduling method. Then, by introducing minimum interval constraint, combined with periodic rescheduling strategy and event-driven rescheduling strategy, the dissertation proposes an improved hybrid rescheduling strategy and provides mathematical analysis.Fourth, designing a generative rescheduling algorithm based on adaptive hybrid optimization. In order to apply the research achievements of optimization theory to spacecraft observation scheduling, based on generative rescheduling and the established model, the dissertation advances discretization of observing time and node representation, thus brings the hybrid optimization algorithm into the field of spacecraft observation scheduling. In view that the algorithm has too many parameters and it is difficult to set the parameter values, this disseration proposes an adaptive hybrid ant colony optimization algorithm (AHACO) and provides mathematical analysis.At last, proposing a repair-based rescheduling algorithm using AHACO. In order to make local adjustments to the existing schedule, to maximize the existing schedule information, to be free from the limitations of uncertainty type, and to enhance the general adaptability, the dissertation brings forward the repair-based rescheduling algorithm on AHACO. After analyzing this problem, this dissertation points out that the foundation of this algorithm is to calculate the affected tasks which can be classified into direct and indirect ones. Then, taking into consideration the features of spacecraft observation rescheduling, this dissertation presents a relative statistical algorithm of all affected tasks based on temporal constraint. In the end, this dissertation presents a repair-based rescheduling algorithm based on pheromone local updating, and using the research results of the foregoing chapter, it applies the AHACO to this repair-based rescheduling method and provides mathematical analysis.