PHEV powertrain co-design with vehicle performance considerations using MDSDO

Azad, Saeed; Behtash, Mohammad; Houshmand, Arian; Alexander-Ramos, Michael J.

doi:10.1007/s00158-019-02264-0

Cited by 22 publications

(1 citation statement)

References 37 publications

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“…Both the nested formulation and the simultaneous formulation adopt direct transcription technique, 10 one category of discretize-then optimize methods, to transcribe the CCD problem into a finitedimensional nonlinear programming (NLP) problem at the time grid nodes firstly, then utilize gradient-based optimizer 11 to solve NLP and obtain the optimal solution of CCD. So far, the nested and simultaneous formulations are broadly applied to engineering practices, such as industrial robotics, 12 rehabilitation robots, 13 active suspension, 14 PHE powertrain, 15 and horizontal axis wind turbines. 4,16 The above CCD formulations have been successfully deployed on the dynamic system in a deterministic manner.…”

Section: Introductionmentioning

confidence: 99%

A Dendrite Net based decoupled framework for the reliability‐based control co‐design problem

Zhang,

Wu,

Qiao

2023

Quality & Reliability Eng

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To improve the reliability of the dynamic system including physical and control design, the reliability‐based control co‐design (RB‐CCD) problem has been studied to account for the uncertainty stemming from the random physical design. However, when encountering RB‐CCD in the sophisticated system in which the dynamic model simulation is time‐consuming or the state equation is expressed implicitly, the available RB‐CCD methods will consume significant computational effort to perform numerous system simulations for the reliability analysis and deterministic optimization. Therefore, this work proposes a Dendrite Net‐based decoupled framework for RB‐CCD to alleviate the computational burden. Specifically, the Dendrite (DD) model constructed by the suggested training scheme integrated with an adaptive sampling strategy is used to approximate the state equation in the dynamic system. After that, the sequential optimization and reliability assessment method decouples RB‐CCD into the control co‐design (CCD) problem and time‐dependent reliability assessment problem, which are solved sequentially based on the cheap estimations of DD model, rather than the expensive simulations of the original system. Furthermore, two numerical examples and an engineering example of 3‐DOF robot system are applied to demonstrate the feasibility and efficiency of the proposed framework.

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