0
Research Papers

Experimentally Infused Plant and Controller Optimization Using Iterative Design of Experiments—Theoretical Framework and Airborne Wind Energy Case Study

[+] Author and Article Information
Nihar Deodhar

Department of Mechanical Engineering,
University of North Carolina at Charlotte,
Charlotte, NC 28223
e-mail: ndeodhar@uncc.edu

Joseph Deese

Department of Mechanical Engineering,
University of North Carolina at Charlotte,
Charlotte, NC 28223
e-mail: jdeese23@uncc.edu

Christopher Vermillion

Mem. ASME
Department of Mechanical Engineering,
University of North Carolina at Charlotte,
Charlotte, NC 28223
e-mail: cvermill@uncc.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 20, 2016; final manuscript received June 1, 2017; published online August 29, 2017. Assoc. Editor: Dumitru I. Caruntu.

J. Dyn. Sys., Meas., Control 140(1), 011004 (Aug 29, 2017) (10 pages) Paper No: DS-16-1603; doi: 10.1115/1.4037014 History: Received December 20, 2016; Revised June 01, 2017

This research presents an iterative framework for optimizing the plant and controller for complex systems by fusing expensive but valuable experiments with cheap yet less accurate simulations. At each iteration, G-optimal design is used to generate experiments and simulations within a prescribed design space that is shrunken in size after each successful iteration. The shrinking of the design space is determined through statistical characterization of a response surface model, and further shrinking is achieved at successive iterations through a numerical model correction factor that is driven by the results of experiments. An initial validation of this iterative design optimization framework was performed on an airborne wind energy (AWE) system, where tethers and an aerostat are used in place of a tower to elevate the turbine to high altitudes. Using a unique lab-scale setup for the experiments, the aforementioned iterative methodology was used to optimize the center of mass location and pitch angle set point for the airborne wind energy system. The optimum configuration yielded a substantial improvement in system responses as compared to a numerically optimized configuration. The framework was recently extended to include four variables (horizontal and vertical stabilizer areas, center of mass location, and pitch angle set point).

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Flowchart demonstrating the sequence of events involved with an iteration of the experimentally infusedoptimization process. In short, the process involves the design of experiments, running the simulations/experiments and generating a response surface, learning from the experiments, and reducing the size of the design space for the next iteration.

Grahic Jump Location
Fig. 2

Left full-scale prototype of an Altaeros BAT used during 2013 flight testing [12]. Right: ground-frame and body-frame coordinates, along with key variables used in deriving equations of motion for the BAT.

Grahic Jump Location
Fig. 3

Block diagram of the flight controller used to track altitude and attitude set points [24]. The present research uses fixed roll and altitude setpoint values.

Grahic Jump Location
Fig. 4

Water channel-based test platform at UNC Charlotte

Grahic Jump Location
Fig. 5

Isometric and bottom views of the 1/100-scale BAT model used for testing. The ballast holes provide the ability to modify the center of mass location between iterations.

Grahic Jump Location
Fig. 6

The experiments and numerical simulation points generated by G-optimal design at every iteration are shown in the left half. The right half shows surface plots of combined numerical and experimental response surface (Ĵ) for every (pp, pc) combination in the shrunken design space.

Grahic Jump Location
Fig. 7

The left and right halves of this figure show a comparison between the numerically optimal configuration and experimentally infused optimum, respectively. A pronounced improvement in pitch angle and heading angle error with experimental infusion is evident from the plots.

Grahic Jump Location
Fig. 8

This figure shows a comparison between pitch angle and heading angle responses for the numerically optimized design (left) versus the design resulting from the experimentally infused optimization approach (right)

Grahic Jump Location
Fig. 9

The experimentally infused optimum results in a slightly higher zenith angle than the numerically optimized design. Thus, the improvement in flight quality seen in Fig. 8 comes at the (relatively minor) cost of an increase in ground footprint.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In