Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140104
Title: Fuzzy model predictive control of discrete-time systems with time-varying delay and disturbances
Authors: Teng, Long
Wang, Youyi
Cai, Wenjian
Li, Hua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2017
Source: Teng, L., Wang, Y., Cai, W., & Li, H. (2018). Fuzzy model predictive control of discrete-time systems with time-varying delay and disturbances. IEEE Transactions on Fuzzy Systems, 26(3), 1192-1206. doi:10.1109/TFUZZ.2017.2717798
Journal: IEEE Transactions on Fuzzy Systems 
Abstract: In this paper, model predictive control (MPC) of discrete T-S fuzzy systems subjected to bounded time-varying delay and persistent disturbances is investigated. The Razumikhin approach is adopted for time-delay systems because it involves a Lyapunov function associated with the original nonaugmented state space of system dynamics when compared to the Krasovskii approach. As such, the Razumikhin approach has a good potential to avoid the inherent complexity of the Krasovskii approach especially in the presence of large delays and disturbances. Based on which, both online and offline MPC approaches for systems with time-varying delay are provided. In addition, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are realized. In the offline approach, a sequence of explicit control laws that correspond to a sequence of robust constraints sets are computed offline. And it is proved that system states including all possibly delayed states can be steered to the terminal constraint set in finite time. Moreover, it allows the exact time delay to be unknown in the proposed two approaches. In particular, for systems with time-varying delay, the special positively invariant set theory and finite-time control theory based on the Razumikhin approach are directly revealed via the proposed offline approach.
URI: https://hdl.handle.net/10356/140104
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2017.2717798
Rights: © 2017 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.2017.2717798
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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