Open AccessSelf‐triggered robust model predictive control for nonlinear systems with bounded disturbances
Author(s) -
Su Yanxu,
Wang Qingling,
Sun Changyin
Publication year - 2019
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.5459
Subject(s) - control theory (sociology) , model predictive control , bounded function , nonlinear system , robust control , nonlinear model , computer science , control (management) , robustness (evolution) , control engineering , mathematics , engineering , artificial intelligence , physics , mathematical analysis , quantum mechanics , biochemistry , chemistry , gene
A self‐triggered model predictive control (MPC) scheme for continuous‐time perturbed nonlinear systems subject to bounded disturbances is investigated in this study. A self‐triggered strategy is designed to obtain the inter‐execution time before the next trigger using the current sampled state. An optimisation problem is addressed to obtain the optimal control trajectory at each triggered instant. The so‐called dual‐mode approach is used to stabilise the perturbed closed‐loop system. Furthermore, sufficient conditions are derived to ensure the feasibility and stability, respectively. It is shown that with a properly designed prediction horizon, the feasibility of the proposed self‐triggered MPC algorithm can be guaranteed if the disturbance is bounded in a small enough area. Meanwhile, the stability is proved under the self‐triggered condition. Finally, a numerical example is given to illustrate the efficacy of the authors proposed scheme.