A Markovian state-space framework for integrating flexibility into space system design decisions

DARPA;NASA;Fractionation;Human space exploration;Spaceflight;Markov decision processes;Pareto frontiers;Multi-attribute decision-making;Markov chains;Optimization;Pareto optimality;Systems engineering;System design;Space systems;Dynamic programming;Systems analysis;Evolvability;Flexibility

Lafleur, Jarret Marshall ; Aerospace Engineering

University:Georgia Institute of Technology

Department:Aerospace Engineering

关键词: DARPA;    NASA;    Fractionation;    Human space exploration;    Spaceflight;    Markov decision processes;    Pareto frontiers;    Multi-attribute decision-making;    Markov chains;    Optimization;    Pareto optimality;    Systems engineering;    System design;    Space systems;    Dynamic programming;    Systems analysis;    Evolvability;    Flexibility;   

Others  :  https://smartech.gatech.edu/bitstream/1853/43749/1/lafleur_jarret_m_201205_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period.To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection.Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.