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Research Papers

Bilinear Model Predictive Control of Plasma Keyhole Pipe Welding Process

[+] Author and Article Information
Kun Qian, YuMing Zhang

Department of Electrical and
Computer Engineering,
University of Kentucky,
Lexington, KY 40506

Manuscript received January 11, 2012; final manuscript received August 15, 2013; published online March 26, 2014. Assoc. Editor: Wei Li.

J. Manuf. Sci. Eng 136(3), 031002 (Mar 26, 2014) (10 pages) Paper No: MANU-12-1012; doi: 10.1115/1.4025337 History: Received January 11, 2012; Revised August 15, 2013

Controlled quasi-keyhole plasma arc welding process adjusts the amperage of the peak current to establish a keyhole in a desired time. This keyhole establishment time is the major parameter that controls the consistence of the weld penetration/quality and needs to be accurately controlled. This paper addresses the control of keyhole establishment time during pipe welding around the circumference, in which the gravitational force acting on the weld pool continuously changes. Because of this continuous change, the dynamic model of the controlled process, with the keyhole establishment time as the output and the amperage of the peak current as the input, varies around the circumference during welding. In addition, it is found that this dynamic model is nonlinear. To control this time varying nonlinear process, the authors propose an adaptive bilinear model predictive control (MPC) algorithm. A self-search algorithm is proposed to decouple the input and output in the model to apply the proposed MPC. Experiments confirmed the effectiveness of the developed control system including the adaptive bilinear MPC.

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References

Zhang, Y. M., and Zhang, S. B., 2001, “Efflux Plasma Charge Based Sensing and Control of Joint Penetration During Keyhole Plasma Arc Welding,” Weld. J., 80(7), pp. 157–162.
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Lu, W., Zhang, Y. M., and Lin, W.-Y., 2004, “Nonlinear Interval Model Control of Quasi-Keyhole Arc Welding Process,” Automatica, 40(5), pp. 805–813. [CrossRef]
Qian, K., and Zhang, Y., 2008, “Optimal Model Predictive Control of Plasma Pipe Welding Process,” IEEE International Conference on Automation Science and Engineering, Arlington, VA, August 23–26. [CrossRef]
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Ljung, L., 1997, System Identification-Theory for the User, Prentice-Hall, Upper Saddle River, NJ.
Bemporad, A., Borrelli, F., and Morari, M., 2002, “Model Predictive Control Based on Linear Programming—The Explicit Solution,” IEEE Trans. Autom. Control, 47(12), pp. 1974–1985. [CrossRef]
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Fontes, A. B., Maitelli, A. L., and Cavalcanti, A. L. O., 2004, “Bilinear Compensated Generalized Predictive Control: An Adaptive Approach,” 5th Asian Control Conference, Melbourne, Australia.
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Figures

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Fig. 1

Keyhole plasma arc pipe welding system 18

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Fig. 2

Weld head and torch 19

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Fig. 3

Keyhole sensor principle 20

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Fig. 4

Control system diagram 21

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Fig. 5

Measured current waveform 22

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Fig. 6

Current waveform illustration 23

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Fig. 7

Tp response to the peak current 24

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Fig. 8

PRMS for validation: (a) PRS designed to control the power supply and (b) measure Ip 25

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Fig. 9

Measured Tp in system identification experiment 26

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Fig. 10

PRMS for validation 27

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Fig. 11

Comparison between simulated output and process output 28

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Fig. 12

Experimental result of tracking performance 29

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Fig. 13

Experimental result of change speed 30

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Fig. 14

(a) Caved pipe before welded, (b) caved pipe after welded, and (c) result of change thickness 31

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Fig. 15

Coefficients change in experiment: (a) bilinear model parameters and (b) linear model parameters 32

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