Robust MDSDO for Co-Design of Stochastic Dynamic Systems

Abstract

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.

Co-design and co-simulation for engineering systems: insights from the Sustainable Infrastructure Planning Game

“…Technical co-design in literature refers to simultaneous decision-making across traditionally sequential disciplines enabled by a shared model (i.e., variables, objectives and constraints) as a type of multidisciplinary design optimization (MDO; Allison, Guo & Han 2014;Azad & Alexander-Ramos 2020). Tighter coupling between decisions allows knowledge in one domain to more directly influence another without unnecessary constraints or delay from multiple iterations, resulting in a more desirable solution.…”