Cooperative body-brain coevolutionary synthesis of mechatronic systems

Jiachuan Wang; Zhun Fan; Janis P. Terpenny; Erik D. Goodman

doi:10.1017/S0890060408000152

Cooperative body-brain coevolutionary synthesis of mechatronic systems

Jiachuan Wang, Zhun Fan, Janis P. Terpenny, Erik D. Goodman

Marcus Department of Industrial and Manufacturing Engineering

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body-brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.

Original language	English (US)
Pages (from-to)	219-234
Number of pages	16
Journal	Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume	22
Issue number	3
DOIs	https://doi.org/10.1017/S0890060408000152
State	Published - Aug 2008

All Science Journal Classification (ASJC) codes

Industrial and Manufacturing Engineering
Artificial Intelligence

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10.1017/S0890060408000152

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title = "Cooperative body-brain coevolutionary synthesis of mechatronic systems",

abstract = "To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body-brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.",

author = "Jiachuan Wang and Zhun Fan and Terpenny, {Janis P.} and Goodman, {Erik D.}",

note = "Funding Information: The authors sincerely thank Christian Gagn{\'e} from Universit{\'e} Laval, Qu{\'e}bec, Canada, the author of the Open Beagle Evolutionary Computation Platform, for his valuable collaboration and support in this work. This work was partially funded by NSF DMII-0115211 and NSF IIS-0325279. Any opinions, findings, and conclusions or recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.",

year = "2008",

month = aug,

doi = "10.1017/S0890060408000152",

language = "English (US)",

volume = "22",

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journal = "Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM",

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AU - Wang, Jiachuan

AU - Fan, Zhun

AU - Terpenny, Janis P.

AU - Goodman, Erik D.

N1 - Funding Information: The authors sincerely thank Christian Gagné from Université Laval, Québec, Canada, the author of the Open Beagle Evolutionary Computation Platform, for his valuable collaboration and support in this work. This work was partially funded by NSF DMII-0115211 and NSF IIS-0325279. Any opinions, findings, and conclusions or recommendations presented in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.

PY - 2008/8

Y1 - 2008/8

N2 - To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body-brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.

AB - To support the concurrent design processes of mechatronic subsystems, unified mechatronics modeling and cooperative body-brain coevolutionary synthesis are developed. In this paper, both body-passive physical systems and brain-active control systems can be represented using the bond graph paradigm. Bond graphs are combined with genetic programming to evolve low-level building blocks into systems with high-level functionalities including both topological configurations and parameter settings. Design spaces of coadapted mechatronic subsystems are automatically explored in parallel for overall design optimality. A quarter-car suspension system case study is provided. Compared with conventional design methods, semiactive suspension designs with more creativity and flexibility are achieved through this approach.

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Cooperative body-brain coevolutionary synthesis of mechatronic systems

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