A Decomposition-Based Optimization Algorithm for Combined Plant and Control Design of Interconnected Dynamic Systems

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Mohammad Behtash ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Dynamic Systems ◽  
Optimization Algorithm ◽  
Hybrid Electric Vehicle ◽  
Integrated Design ◽  
Design Methods ◽  
System Level ◽  
Optimal Solutions ◽  
Optimization Strategy ◽  
And Control

Abstract Strong coupling of the physical and control parts within complex dynamic systems should be addressed by integrated design approaches that can manage such interactions. Otherwise, the final solution will be suboptimal or even infeasible. Combined design and control (co-design) methods can tackle this issue by managing the mentioned interactions and can result in superior optimal solutions. Current co-design methods are applicable to simplified non-interconnected systems; however, these methods might be impractical or even impossible to apply to real-world interconnected dynamic systems, hindering designers from obtaining the system-level optimal solutions. This work addresses this issue by developing an optimization algorithm which combines a decomposition-based optimization strategy known as analytical target cascading (ATC) with a co-design-centric formulation of multidisciplinary dynamic system design optimization (MDSDO). Considering the time-dependent linking variables among the dynamic systems’ components, a new consistency measure has also been proposed to manage such quantities in the optimization process. Finally, a plug-in hybrid electric vehicle powertrain, representative of an interconnected dynamic system, has been studied to validate the new algorithm’s results against the conventional all-at-once (AAO) MDSDO. Although the numerical results from the ATC-MDSDO slightly deviate from those in the AAO-MDSDO, this method can play a crucial role as a benchmark when the AAO solution is unattainable or a distributed design paradigm is required.

Human Driver Modeling Based on Analytical Optimal Solutions: Stopping Behaviors at the Intersections

2019 ◽  
Author(s):  
Jihun Han ◽  
Dominik Karbowski ◽  
Namdoo Kim ◽  
Aymeric Rousseau
Keyword(s):  
Dynamic Systems ◽  
Optimal Solutions ◽  
Human Driver ◽  
Systems And Control ◽  
And Control

Abstract Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046575/1075675/Human-Driver-Modeling-Based-on-Analytical-Optimal

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

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 an 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 the importance of the robust approach on the integrated design solutions and performance measures.

A Combined Plant/Controller Optimization Framework for Hybrid Vehicles With MPG, Emissions and Drivability Considerations

2016 ◽  
Author(s):  
Huckleberry Febbo ◽  
Tulga Ersal ◽  
Jeffrey L. Stein
Keyword(s):  
Hybrid Electric Vehicle ◽  
Optimization Problem ◽  
Design Procedure ◽  
Design Tool ◽  
Optimized Design ◽  
Optimization Strategy ◽  
Meta Model ◽  
Design Methodologies ◽  
Trade Offs ◽  
And Control

The design and control of hybrid-electric vehicle (HEV) powertrains presents an optimization problem to balance the trade-off between multiple objectives, such as fuel economy, driv-ability, and emissions. However, current design methodologies do not simultaneously incorporate all of these three considerations into both the sizing and control layers of the optimization problem. This paper first demonstrates that the trade-offs between these objectives can be non-trivial in the HEV control problem. This motivates the need for a systematic design procedure that can take all three objectives into account. To address this need, the paper describes the development of a new and efficient design framework called the Hybrid-Vehicle Design Tool (HVDT), which adopts a bi-level optimization strategy. Efficiency is achieved by introducing a neural-network-based meta-model to predict the performance of the optimal control strategy obtained using Dynamic Programming (DP). To demonstrate the HVDT, a small HEV is designed for the UDDS and HWFET driving cycles separately. Results show that the optimized design can reduce fuel consumption, improve emissions and improve driv-ability when compared to the nominal design obtained using first principle design methodologies. Additionally, compared to using DP directly in the bi-level optimization, using the meta-model reduces the simulation from 238 to 16 days (93%) and from 132 to 16 days (88%) for the UDDS and HWFET cycles, respectively, with an acceptable compromise in the accuracy of predicting the performance of DP.

Decomposition-Based MDSDO for Co-Design of Large-Scale Dynamic Systems

2018 ◽  
Author(s):  
Mohammad Behtash ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Dynamic Systems ◽  
Large Scale ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Design Algorithm ◽  
System Solution ◽  
New Formulation

Conventional sequential methods are not bound to yield optimal solutions for design of physical systems and their corresponding control systems. However, by managing the interactions, combined physical and control system design (co-design) can produce superior optimal results. Existing Co-design methods are practical for moderate-scale systems; whereas, they can be impractical or impossible to use when applied to large-scale systems and consequently may limit our determination of an optimal solution. This work addresses this issue by developing a novel decomposition-based version of a co-design algorithm to optimize such large-scale dynamic systems. The new formulation implements a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) of a large-scale dynamic system. In addition, a new consistency measure was also established to manage time-dependent linking variables. Results substantiate the ability of the new formulation in identifying the optimal dynamic system solution.

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2018 ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

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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