Details
| Original language | English |
|---|---|
| Title of host publication | Systems, Design, and Complexity |
| ISBN (electronic) | 9780791850657 |
| Publication status | Published - 2016 |
Abstract
Multiobjective multidisciplinary optimization supports the development of mechatronic systems. A suitable approach is required for a short calculation time and sufficient results. All aspects of the system model (mechatronic system, cost model and time optimal control problem) are incorporated into one nonlinear optimization model, following the all-at-once approach. The dynamic simulation is discretized in time and optimization variables are introduced for the state at each time step. Formulating the problem in an algebraic modeling language and solving it by the interior point method allows very fast solution times. This enables a fast turnaround time during the preliminary design phase in the product development. Sensitivities of the objective with respect to model parameters and design constraints are generated by the solver and used to guide the modeling and development process. Using these sensitivities, the model can be improved where necessary while keeping the model complexity low by simplifying less important parts. As example an electromechanical actuating system is considered, in which the rotary motion of a motor is converted to a translational movement with a gate-tape gear.
Keywords
- All-atonce approach, Mechatronic system, Multiobjective multidisciplinary optimization, Nonlinear optimization problem, Optimal control, Sensitivities
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
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Systems, Design, and Complexity. 2016.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Fast multidisciplinary design optimization in the development of mechatronic systems
AU - Reul, A.
AU - Schwerdt, L.
AU - Rinderknecht, S.
N1 - Publisher Copyright: Copyright © 2016 by ASME. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - Multiobjective multidisciplinary optimization supports the development of mechatronic systems. A suitable approach is required for a short calculation time and sufficient results. All aspects of the system model (mechatronic system, cost model and time optimal control problem) are incorporated into one nonlinear optimization model, following the all-at-once approach. The dynamic simulation is discretized in time and optimization variables are introduced for the state at each time step. Formulating the problem in an algebraic modeling language and solving it by the interior point method allows very fast solution times. This enables a fast turnaround time during the preliminary design phase in the product development. Sensitivities of the objective with respect to model parameters and design constraints are generated by the solver and used to guide the modeling and development process. Using these sensitivities, the model can be improved where necessary while keeping the model complexity low by simplifying less important parts. As example an electromechanical actuating system is considered, in which the rotary motion of a motor is converted to a translational movement with a gate-tape gear.
AB - Multiobjective multidisciplinary optimization supports the development of mechatronic systems. A suitable approach is required for a short calculation time and sufficient results. All aspects of the system model (mechatronic system, cost model and time optimal control problem) are incorporated into one nonlinear optimization model, following the all-at-once approach. The dynamic simulation is discretized in time and optimization variables are introduced for the state at each time step. Formulating the problem in an algebraic modeling language and solving it by the interior point method allows very fast solution times. This enables a fast turnaround time during the preliminary design phase in the product development. Sensitivities of the objective with respect to model parameters and design constraints are generated by the solver and used to guide the modeling and development process. Using these sensitivities, the model can be improved where necessary while keeping the model complexity low by simplifying less important parts. As example an electromechanical actuating system is considered, in which the rotary motion of a motor is converted to a translational movement with a gate-tape gear.
KW - All-atonce approach
KW - Mechatronic system
KW - Multiobjective multidisciplinary optimization
KW - Nonlinear optimization problem
KW - Optimal control
KW - Sensitivities
UR - http://www.scopus.com/inward/record.url?scp=85021632397&partnerID=8YFLogxK
U2 - 10.1115/IMECE201665599
DO - 10.1115/IMECE201665599
M3 - Conference contribution
BT - Systems, Design, and Complexity
ER -