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Research Papers

An Iterative Learning Control Approach to Improving Fidelity in Internet-Distributed Hardware-in-the-Loop Simulation

[+] Author and Article Information
Tulga Ersal

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: tersal@umich.edu

Mark Brudnak

The US Army Tank-Automotive Research,
Development and Engineering Center,
Warren, MI 48397
e-mail: mark.j.brudnak.civ@mail.mil

Ashwin Salvi

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: asalvi@umich.edu

Youngki Kim

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: youngki@umich.edu

Jason B. Siegel

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: siegeljb@umich.edu

Jeffrey L. Stein

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: stein@umich.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 16, 2013; final manuscript received June 12, 2014; published online August 8, 2014. Assoc. Editor: Gregory Shaver. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Dyn. Sys., Meas., Control 136(6), 061012 (Aug 08, 2014) (8 pages) Paper No: DS-13-1276; doi: 10.1115/1.4027868 History: Received July 16, 2013; Revised June 12, 2014

One of the main challenges of cosimulating hardware-in-the-loop (HIL) systems in real-time over the Internet is the fidelity of the simulation. The dynamics of the Internet may significantly distort the dynamics of the network-integrated system. This paper presents the development and experimental validation of an iterative learning control (ILC) based approach to improve fidelity of such networked system integration. Toward this end, a new metric for characterizing coupling fidelity is proposed, which, unlike some existing metrics, enables the formulation of the problem of improving system fidelity without requiring any knowledge about the reference dynamics (i.e., dynamics that would be observed, if the system was physically connected). Next, using this metric, the problem of improving fidelity is formulated as an ILC problem. The proposed approach is illustrated on an experimental setup simulating a hybrid electric powertrain distributed across three different sites with a real engine and battery in the loop. The conclusion is that the proposed approach holds significant potential for achieving high fidelity in Internet-distributed HIL (ID-HIL) simulation setups.

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References

Figures

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Fig. 1

Illustration of defining the errors in the coupling variables toward characterizing fidelity in ID-HIL on an example with two sites (Systems 1 and 2). ci,j represents the i-th coupling variable on the system j side of the network, and ei represents the instantaneous error in the i-th coupling variable.

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Fig. 2

Proposed ILC-based framework for iteratively improving ID-HIL fidelity. This figure illustrates a decentralized approach in which an independent ILC controller is utilized for each coupling variable.

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Fig. 3

The overview of the system considered in the case study. Each shaded region corresponds to a different geographic location. Italic typeface denotes physical components; the remaining components are modeled.

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Fig. 4

A photo of the engine-in-the-loop testing facility

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Fig. 5

The battery testing laboratory

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Fig. 6

Packet delays observed during a network characterization test

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Fig. 7

The speed profile used in the case study is a portion of the Urban Assault Cycle

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Fig. 8

ILC performance in the case study

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Fig. 9

Improvement in the fidelity of some output variables due to the proposed ILC framework

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Fig. 10

Battery power as a representative system response to illustrate how internet delay can affect system dynamics and how the proposed method can alleviate its negative impact

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Fig. 11

Efficiency contour map of an electric motor superimposed by maximum and continuous torque

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Fig. 12

BSFC of engine/generator unit obtained by combining engine BSFC and generator efficiency and superimposed by optimal operation lines of the engine/generator unit and the engine only

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Fig. 13

The schematic diagram of the FDPD strategy

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