Research Papers: Design Automation

A Problem Class With Combined Architecture, Plant, and Control Design Applied to Vehicle Suspensions

[+] Author and Article Information
Daniel R. Herber

Department of Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: herber1@illinois.edu

James T. Allison

Department of Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: jtalliso@illinois.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received September 26, 2018; final manuscript received March 17, 2019; published online May 13, 2019. Assoc. Editor: Scott Ferguson.

J. Mech. Des 141(10), 101401 (May 13, 2019) (11 pages) Paper No: MD-18-1711; doi: 10.1115/1.4043312 History: Received September 26, 2018; Accepted March 20, 2019

Here we describe a problem class with combined architecture, plant, and control design for dynamic engineering systems. The design problem class is characterized by architectures comprised of linear physical elements and nested co-design optimization problems employing linear-quadratic dynamic optimization. The select problem class leverages a number of existing theory and tools and is particularly effective due to the symbiosis between labeled graph representations of architectures, dynamic models constructed from linear physical elements, linear-quadratic dynamic optimization, and the nested co-design solution strategy. A vehicle suspension case study is investigated and a specifically constructed architecture, plant, and control design problem is described. The result was the automated generation and co-design problem evaluation of 4374 unique suspension architectures. The results demonstrate that changes to the vehicle suspension architecture can result in improved performance, but at the cost of increased mechanical complexity. Furthermore, the case study highlights a number of challenges associated with finding solutions to the considered class of design problems. One such challenge is the requirement to use simplified design problem elements/models; thus, the goal of these early-stage studies are to identify new architectures that are worth investigating more deeply. The results of higher-fidelity studies on a subset of high-performance architectures can then be used to select a final system architecture. In many aspects, the described problem class is the simplest case applicable to graph-representable, dynamic engineering systems.

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Fig. 1

Some vehicle suspension architectures: (a) canonical passive [4,6]; (b) pure active [2,3]; (c) canonical active [5,1518]; (d) active with dynamic absorber [2,3]; and (e) an alternative active architecture

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Fig. 2

Different representations of the canonical active suspension architecture in Fig. 1(c): (a) undirected labeled graph and (b) simscape/simulink model

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Fig. 3

The proposed trilevel solution strategy for combined architecture, plant, and control design of a dynamic system

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Fig. 4

Vehicle suspension architecture design domain

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Fig. 5

Summary of the results for 4351 candidate suspensions: (a) performance versus percentile rank and (b) performance versus complexity

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Fig. 6

Results for the pure active architecture (Nc = 1): (a) architecture, (b) positions, (c) control, and (d) rattlespace and stroke limits

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Fig. 7

Results for the canonical active architecture (Nc = 3): (a) architecture, (b) positions, (c) control, and (d) rattlespace and stroke limits

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Fig. 8

Results for best performing architecture with Nc = 3: (a) architecture, (b) positions, (c) control, and (d) rattlespace and stroke limits

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Fig. 9

Results for best performing architecture with Nc = 4: (a) architecture, (b) positions, (c) control, and (d) rattlespace and stroke limits

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Fig. 10

Results for best performing architecture with Nc = 5: (a) architecture, (b) positions, (c) control, and (d) rattlespace and stroke limits

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Fig. 11

Results for the best performing architecture with Nc = 7: (a) architecture, (b) positions, (c) control, and (d) rattlespace and stroke limits

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Fig. 12

The objective-plant space for two candidate architectures with two plant variables with the minimum marked with a circle: (a) convex behavior for Fig. 7(a) and (b) nonconvex behavior for Fig. 8(a)



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