Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142450
Title: Robust fuzzy model predictive control of discrete-time Takagi–Sugeno systems with nonlinear local models
Authors: Teng, Long
Wang, Youyi
Cai, Wenjian
Li, Hua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Teng, L., Wang, Y., Cai, W., & Li, H. (2018). Robust fuzzy model predictive control of discrete-time Takagi–Sugeno systems with nonlinear local models. IEEE Transactions on Fuzzy Systems, 26(5), 2915-2925. doi:10.1109/TFUZZ.2018.2815521
Journal: IEEE Transactions on Fuzzy Systems 
Abstract: Robust fuzzy model predictive control of discrete nonlinear systems is investigated in this paper. A recently developed Takagi-Sugeno (T-S) fuzzy approach which uses nonlinear local models is adopted to approximate the nonlinear systems. A critical issue that restricts the practical application of classical model predictive control is the online computational cost. For model predictive control of T-S fuzzy systems, the online computational burden is even worse. Especially for complex systems with severe nonlinearities, parametric uncertainties, and disturbances, existing model predictive control of T-S fuzzy systems usually leads to a very conservative solution or even no solution in some occasions. However, more relaxed results can be achieved by the proposed fuzzy model predictive control approach which adopts T-S systems with nonlinear local models. Another advantage is that online computational cost of the optimization problem through solving matrix inequalities can be significantly reduced at the same time. Simulations on a numerical example and a two-tank system are presented to verify the effectiveness and advantages of the proposed method. Comparisons among several T-S fuzzy approaches are illustrated and show that the best settling time is achieved via the proposed method.
URI: https://hdl.handle.net/10356/142450
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2018.2815521
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TFUZZ.2018.2815521
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles

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