By Topic

Game theory and possibilistic logic to face the space mission preliminary design optimization: a coevolutive architecture with a semicooperative protocol

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
M. Lavagna ; Dip. Ingegneria Aerospaziale, Politecnico di Milano, Milan ; A. Mafficini

The paper presents a possible approach for supporting the preliminary space system design in the very early phases. The proposed method generates the inputs for the so-called pre-phase. A feasibility study, the engineers' team works on to define a preliminary space system solution. Space missions, particularly focusing on interplanetary exploration, are fast growing in complexity: several modules can be involved in the same mission such as multiple orbiters, entry modules, landers/rovers, ascending vehicles, and, eventually, human transportation units; missions can be split into more different phases, according to particular operative modes of each module; mission objectives and requirements can be definitely ambitious and demanding. Such various scenario identifies a hyperspace in which a rough mission configuration must be selected to begin the quantitative space system design. However, at the very beginning of the mission design, the qualitative mission objectives and related possible requirements only are available as inputs. The proposed tool, starting from those generic and qualitative mission objectives, not only generates all possible high level architectures, but, thanks to a co-evolutive multi-objective optimization algorithm, sorts the final Pareto solutions, according to a predefined metric. No system sizing is accomplished; that is why the criteria vector is, here, represented by a set of indexes specifically and robustly modeled thanks to the possibilistic logic theory, the statistical approach and the fuzzy logic theory. A co-evolutive approach has been selected because of the extended dimension of the criteria vector. In particular, the game theory is applied with a two-player architecture with a semi-cooperative protocol to manage the systematic information exchange and the population selection to be carried on in the optimization process. Comparisons with the classical non-cooperative and fully cooperative protocols show the validity of the propos- ed negotiation strategy to converge to the Pareto front for the whole set of variables and cost functions. The co-evolutive algorithm showed very good performance according to the classical test functions available in literature

Published in:

Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

Date of Conference:

24-28 July 2005