Robust MDSDO for Co-Design of Stochastic Dynamic Systems

[+] Author and Article Information
Saeed Azad

3419 Brookline Ave Apt 3 Cincinnati, OH 45220-1849 azadsd@mail.uc.edu

Michael J. Alexander-Ramos

688 Rhodes Hall 2851 Woodside Drive University of Cincinnati Cincinnati, OH 45221 alexanmj@ucmail.uc.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received October 29, 2018; final manuscript received July 13, 2019; published online xx xx, xxxx. Assoc. Editor: Xiaoping Du.

ASME doi:10.1115/1.4044430 History: Received October 29, 2018; Accepted July 17, 2019


Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well-established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for a RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating a significant impact of the robust approach on the integrated design solutions and performance measures.

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