Research Papers

Passive Fuzzy Control via Fuzzy Integral Lyapunov Function for Nonlinear Ship Drum-Boiler Systems

[+] Author and Article Information
Wen-Jer Chang

Department of Marine Engineering,
National Taiwan Ocean University,
Keelung City 202, Taiwan, China
e-mail: wjchang@mail.ntou.edu.tw

Yao-Chung Chang

Department of Marine Engineering,
National Taiwan Ocean University,
Keelung City 202, Taiwan, China
e-mail: jason_chang780312@hotmail.com

Cheung-Chieh Ku

Department of Marine Engineering,
National Taiwan Ocean University,
Keelung City 202, Taiwan, China
e-mail: ccku@mail.ntou.edu.tw

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 29, 2014; final manuscript received August 28, 2014; published online November 7, 2014. Assoc. Editor: Ryozo Nagamune.

J. Dyn. Sys., Meas., Control 137(4), 041008 (Apr 01, 2015) (10 pages) Paper No: DS-14-1046; doi: 10.1115/1.4028608 History: Received January 29, 2014; Revised August 28, 2014

The passive fuzzy control problem is studied in this paper via line-integral fuzzy Lyapunov function for nonlinear ship drum-boiler systems with multiplicative noises. The Takagi-Sugeno (T-S) fuzzy model is employed to represent the nonlinearities of the multiplicative noised nonlinear ship drum-boiler systems. For stability analysis and synthesis, the sufficient conditions are derived via line-integral fuzzy Lyapunov functions. These conditions are transformed into the linear matrix inequality (LMI) forms which can be solved by the convex optimal programming algorithm. In addition, the passivity theory is employed to deal with the effect of external disturbance in the ship drum-boiler system. Finally, a numerical example is provided to show the applicability and effectiveness of the proposed fuzzy controller design approach.

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Grahic Jump Location
Fig. 1

Structure of nonlinear ship drum-boiler system

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Fig. 2

Membership function of x1(t)

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Fig. 3

Flowchart of ILMI algorithm

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Fig. 4

Responses of measured drum pressure q1(t)

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Fig. 5

Responses of excess oxygen level q2(t)

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Fig. 6

Responses of drum water level q3(t)

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Fig. 7

Responses of steam flow rate q4(t)

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Fig. 8

Comparisons between proposed fuzzy controller and PI controller for q1(t)

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Fig. 9

Comparisons between proposed fuzzy controller and PI controller for q2(t)

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Fig. 10

Comparisons between proposed fuzzy controller and PI controller for q3(t)

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Fig. 11

Comparisons between proposed fuzzy controller and PI controller for q4(t)




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