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Research Papers

Multimodel Control of Nonlinear Systems: An Improved Gap Metric and Stability Margin-Based Method

[+] Author and Article Information
Mahdi Ahmadi

Advanced Control Systems Lab,
Electrical Engineering,
Sharif University of Technology,
Tehran 11155-4363, Iran

Mohammad Haeri

Advanced Control Systems Lab,
Electrical Engineering,
Sharif University of Technology,
Tehran 11155-4363, Iran
e-mail: haeri@sina.sharif.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received June 10, 2017; final manuscript received December 13, 2017; published online March 13, 2018. Assoc. Editor: Davide Spinello.

J. Dyn. Sys., Meas., Control 140(8), 081013 (Mar 13, 2018) (12 pages) Paper No: DS-17-1298; doi: 10.1115/1.4039086 History: Received June 10, 2017; Revised December 13, 2017

This paper presents a new multimodel controller design approach incorporating stability and performance criteria. The gap metric is employed to measure the distance between local models. An efficient method based on state feedback strategy is introduced to improve the maximum stability margin of the local models. The proposed method avoids local model redundancy, simplifies the multimodel controller structure, and supports employing of many linear control techniques, while does not rely on a priori experience to choose the gridding threshold value. To evaluate the proposed method, three benchmark nonlinear systems are studied. Simulation results demonstrate that the method provides the closed-loop stability and performance via a simple multimodel structure in comparison with the opponents.

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References

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Figures

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Fig. 1

Schematic of the proposed approach

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Fig. 2

Schematic of the MagLev system

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Fig. 3

Gaps between 18 linearized models of the MagLev system

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Fig. 4

Set point tracking of the nonlinear system (17)

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Fig. 5

Disturbance rejection in the nonlinear system (17)

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Fig. 6

Steady-state curve of the system in Eq. (22)

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Fig. 7

(a) The gaps between the local models and (b) the maximum stability margin of the local models

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Fig. 8

Set point tracking of nonlinear system (22)

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Fig. 9

Set point tracking of nonlinear system (22) while there is an uncertainty

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Fig. 10

Steady-state gain of nonisothermal system (23)

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Fig. 11

(a) The gaps between the local models and (b) the maximum stability margin of the local models

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Fig. 12

Set point tracking control of the nonlinear system (23)

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Fig. 13

Disturbance rejection control of the nonlinear system (23)

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Fig. 14

Set point tracking of the nonlinear system (23) while there is an uncertainty

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