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Research Papers

Run-to-Run Optimization Control Within Exact Inverse Framework for Scan Tracking

[+] Author and Article Information
Ivan L. Yeoh

Department of Mechanical Engineering,
University of Washington,
Seattle, WA 98195
e-mail: ivanyeoh@uw.edu

Per G. Reinhall, Martin C. Berg, Eric J. Seibel

Department of Mechanical Engineering,
University of Washington,
Seattle, WA 98195

Howard J. Chizeck

Department of Electrical Engineering,
University of Washington,
Seattle, WA 98195

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received June 7, 2016; final manuscript received February 11, 2017; published online June 5, 2017. Assoc. Editor: Maurizio Porfiri.

J. Dyn. Sys., Meas., Control 139(9), 091011 (Jun 05, 2017) (12 pages) Paper No: DS-16-1296; doi: 10.1115/1.4036231 History: Received June 07, 2016; Revised February 11, 2017

A run-to-run optimization controller uses a reduced set of measurement parameters, in comparison to more general feedback controllers, to converge to the best control point for a repetitive process. A new run-to-run optimization controller is presented for the scanning fiber device used for image acquisition and display. This controller utilizes very sparse measurements to estimate a system energy measure and updates the input parameterizations iteratively within a feedforward with exact-inversion framework. Analysis, simulation, and experimental investigations on the scanning fiber device demonstrate improved scan accuracy over previous methods and automatic controller adaptation to changing operating temperature. A specific application example and quantitative error analyses are provided of a scanning fiber endoscope that maintains high image quality continuously across a 20 °C temperature rise without interruption of the 56 Hz video.

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Figures

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Fig. 1

The general run-to-run optimization algorithm. Highlighted blocks are avenues for control system design.

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Fig. 2

The SFE scan engine consisting of a piezoelectric-tube and a cantilevered optical fiber [1]

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Fig. 3

(a) Desired and achieved trajectories for cases without and with modeling error and (b) energy measure over time for cases without and with modeling error

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Fig. 4

Convex energy surface E(t=200) parameterized by two-variable modeling error

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Fig. 5

Convex energy surface for (a) K=5K0 and M=M0 (left) and (b) K=K0 and M=5M0 (right)

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Fig. 6

Experimental setup

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Fig. 7

Experimentally constructed energy surface

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Fig. 8

At 50 °C: (a) 2D scan result with feedforward control, (b) 2D scan result with run-to-run optimization, (c) normalized squared radial error compared between feed-forward and run-to-run result, and (d) phase/tangential squared error compared between feed-forward and run-to-run result

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Fig. 9

Radial and phase/tangential mean-squared-error (MSE) over different operating temperatures, achieved by feedforward method versus run-to-run optimization: (a) radial MSE and (b) tangential MSE

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Fig. 10

At 50 °C: (a) 2D scan result with open-loop control, (b) 2D scan result with run-to-run optimization, (c) normalized squared radial error compared between open-loop and run-to-run result, and (d) phase/tangential squared error compared between open-loop and run-to-run result

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Fig. 11

Radial and phase/tangential MSE over different operating temperatures, achieved by open-loop method versus run-to-run optimization: (a) radial MSE and (b) tangential MSE

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Fig. 12

(a) Target image to be laser-projected, (b) open-loop control result at 50 °C, (c) adaptive feedforward result at 50 °C, and (d) run-to-run optimized result at 50 °C

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Fig. 13

Simulation of energy surface with 1% amplitude measurement noise

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Fig. 14

Data from experiment 2 recast as pixel/Euclidean MSE over different operating temperatures, achieved by open-loop versus new run-to-run optimization

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