ROSYJun 14, 2020

A Fuzzy-based Framework to Support Multicriteria Design of Mechatronic Systems

arXiv:2006.07790v14 citations
Originality Synthesis-oriented
AI Analysis

This work addresses the need for systematic design methodologies in mechatronics, but it is incremental as it builds on existing fuzzy-based frameworks.

The authors tackled the problem of designing mechatronic systems by proposing three methods for identifying fuzzy measures to improve decision-making in multicriteria design, as demonstrated in a case study of a vision-guided quadrotor drone.

Designing a mechatronic system is a complex task since it deals with a high number of system components with multi-disciplinary nature in the presence of interacting design objectives. Currently, the sequential design is widely used by designers in industries that deal with different domains and their corresponding design objectives separately leading to a functional but not necessarily an optimal result. Consequently, the need for a systematic and multi-objective design methodology arises. A new conceptual design approach based on a multi-criteria profile for mechatronic systems has been previously presented by the authors which uses a series of nonlinear fuzzy-based aggregation functions to facilitate decision-making for design evaluation in the presence of interacting criteria. Choquet fuzzy integrals are one of the most expressive and reliable preference models used in decision theory for multicriteria decision making. They perform a weighted aggregation by the means of fuzzy measures assigning a weight to any coalition of criteria. This enables the designers to model importance and also interactions among criteria thus covering an important range of possible decision outcomes. However, specification of the fuzzy measures involves many parameters and is very difficult when only relying on the designer's intuition. In this paper, we discuss three different methods of fuzzy measure identification tailored for a mechatronic design process and exemplified by a case study of designing a vision-guided quadrotor drone. The results obtained from each method are discussed in the end.

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