Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models

Long Teng; Youyi Wang; Wenjian Cai; Hua Li

doi:10.1109/ICCA.2016.7505255

Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models

Long Teng, Youyi Wang, Wenjian Cai, Hua Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

This paper presents a robust model predictive control method for discrete nonlinear systems. Instead of conventional T-S fuzzy system where linear local models are used, T-S fuzzy system with nonlinear local models is adopted that the number of fuzzy rules is decreased and the computational burden is reduced. Meanwhile, persistent external disturbances are also considered in the T-S fuzzy systems that input-to-state stability is realized. Based on the concept of robust positively invariant set, the terminal constraint set for T-S fuzzy systems with nonlinear local models is built. The advantages of the developed method is demonstrated in simulation by comparison with an existing fuzzy model predictive control method with linear local models.

Original language	English
Title of host publication	12th IEEE International Conference on Control and Automation, ICCA 2016
Publisher	IEEE Computer Society
Pages	74-79
Number of pages	6
ISBN (Electronic)	9781509017386
DOIs	https://doi.org/10.1109/ICCA.2016.7505255
Publication status	Published - 7 Jul 2016
Externally published	Yes
Event	12th IEEE International Conference on Control and Automation, ICCA 2016 - Kathmandu, Nepal Duration: 1 Jun 2016 → 3 Jun 2016

Publication series

Name	IEEE International Conference on Control and Automation, ICCA
Volume	2016-July
ISSN (Print)	1948-3449
ISSN (Electronic)	1948-3457

Conference

Conference	12th IEEE International Conference on Control and Automation, ICCA 2016
Country/Territory	Nepal
City	Kathmandu
Period	1/06/16 → 3/06/16

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Control and Systems Engineering
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering

More information

10.1109/ICCA.2016.7505255

Cite this

Teng, L., Wang, Y., Cai, W., & Li, H. (2016). Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models. In 12th IEEE International Conference on Control and Automation, ICCA 2016 (pp. 74-79). Article 7505255 (IEEE International Conference on Control and Automation, ICCA; Vol. 2016-July). IEEE Computer Society. https://doi.org/10.1109/ICCA.2016.7505255

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title = "Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models",

abstract = "This paper presents a robust model predictive control method for discrete nonlinear systems. Instead of conventional T-S fuzzy system where linear local models are used, T-S fuzzy system with nonlinear local models is adopted that the number of fuzzy rules is decreased and the computational burden is reduced. Meanwhile, persistent external disturbances are also considered in the T-S fuzzy systems that input-to-state stability is realized. Based on the concept of robust positively invariant set, the terminal constraint set for T-S fuzzy systems with nonlinear local models is built. The advantages of the developed method is demonstrated in simulation by comparison with an existing fuzzy model predictive control method with linear local models.",

author = "Long Teng and Youyi Wang and Wenjian Cai and Hua Li",

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year = "2016",

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doi = "10.1109/ICCA.2016.7505255",

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Teng, L, Wang, Y, Cai, W & Li, H 2016, Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models. in 12th IEEE International Conference on Control and Automation, ICCA 2016., 7505255, IEEE International Conference on Control and Automation, ICCA, vol. 2016-July, IEEE Computer Society, pp. 74-79, 12th IEEE International Conference on Control and Automation, ICCA 2016, Kathmandu, Nepal, 1/06/16. https://doi.org/10.1109/ICCA.2016.7505255

Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models. / Teng, Long; Wang, Youyi; Cai, Wenjian et al.
12th IEEE International Conference on Control and Automation, ICCA 2016. IEEE Computer Society, 2016. p. 74-79 7505255 (IEEE International Conference on Control and Automation, ICCA; Vol. 2016-July).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - This paper presents a robust model predictive control method for discrete nonlinear systems. Instead of conventional T-S fuzzy system where linear local models are used, T-S fuzzy system with nonlinear local models is adopted that the number of fuzzy rules is decreased and the computational burden is reduced. Meanwhile, persistent external disturbances are also considered in the T-S fuzzy systems that input-to-state stability is realized. Based on the concept of robust positively invariant set, the terminal constraint set for T-S fuzzy systems with nonlinear local models is built. The advantages of the developed method is demonstrated in simulation by comparison with an existing fuzzy model predictive control method with linear local models.

AB - This paper presents a robust model predictive control method for discrete nonlinear systems. Instead of conventional T-S fuzzy system where linear local models are used, T-S fuzzy system with nonlinear local models is adopted that the number of fuzzy rules is decreased and the computational burden is reduced. Meanwhile, persistent external disturbances are also considered in the T-S fuzzy systems that input-to-state stability is realized. Based on the concept of robust positively invariant set, the terminal constraint set for T-S fuzzy systems with nonlinear local models is built. The advantages of the developed method is demonstrated in simulation by comparison with an existing fuzzy model predictive control method with linear local models.

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Robust model predictive control for discrete T-S fuzzy systems with nonlinear local models

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