Efficient Robust Fuzzy Model Predictive Control of Discrete Nonlinear Time-Delay Systems via Razumikhin Approach

Long Teng, Youyi Wang, Wenjian Cai, Hua Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)

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.

Original languageEnglish
Article number8401911
Pages (from-to)262-272
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume27
Issue number2
DOIs
Publication statusPublished - Feb 2019
Externally publishedYes

Keywords

  • Lyapunov-Razumikhin
  • model predictive control (MPC)
  • optimal control
  • Takagi-Sugeno (T-S) fuzzy systems
  • time delay

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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