Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142445
Title: Efficient robust fuzzy model predictive control of discrete nonlinear time-delay systems via Razumikhin approach
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. (2019). Efficient robust fuzzy model predictive control of discrete nonlinear time-delay systems via Razumikhin approach. IEEE Transactions on Fuzzy Systems, 27(2), 262-272. doi:10.1109/TFUZZ.2018.2852305
Journal: IEEE Transactions on Fuzzy Systems 
Abstract: In this paper, two efficient robust fuzzy model predictive control algorithms are investigated for discrete nonlinear systems with multiple time delays and bounded disturbances. The famous Takagi-Sugeno (T-S) fuzzy systems are utilized to represent nonlinear systems. Instead of the Lyapunov-Krasovskii functional, the Lyapunov-Razumikhin function is adopted to deal with time delays because it involves invariant sets in the original state space of the system. A sequence of explicit control laws corresponding to a sequence of constraint sets are computed offline so that the online computational burden associated with the classical model predictive control algorithms is significantly reduced. In particular, the set invariance theory behind the Razumikhin approach, which is more complicated than the one for nondelayed systems, is directly observed. Additionally, it is proved that all (delayed) states can enter the terminal set in finite time. Moreover, robust positive invariance and input-to-state stability for time-delay systems concerning disturbances are realized. Additionally, an online optimization algorithm is also provided based on the offline computed ellipsoidal sets. Therefore, the conservatism induced by the Razumikhin approach is relaxed, while the computational cost is not significantly increased.
URI: https://hdl.handle.net/10356/142445
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2018.2852305
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.2018.2852305.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ERI@N Journal Articles

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