Open AccessDecentralized event‐triggered robust MPC for large‐scale networked Lipchitz non‐linear control systems
Author(s) -
GHorbani Saeid,
Safavi Ali Akbar,
Naghavi S. Vahid
Publication year - 2021
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/cth2.12195
Subject(s) - control theory (sociology) , model predictive control , computer science , controller (irrigation) , information exchange , networked control system , event (particle physics) , stability (learning theory) , linear system , decentralised system , scale (ratio) , control (management) , state (computer science) , limit (mathematics) , control system , engineering , mathematics , artificial intelligence , telecommunications , mathematical analysis , physics , quantum mechanics , machine learning , agronomy , biology , electrical engineering , algorithm
This article examines a decentralized event‐triggered robust model predictive control (MPC) for a class of networked large‐scale non‐linear Lipchitz systems. It is assumed that the subsystems are geographically distributed and the connections can be made over a communication network and therefore local event generator modules are used. An event‐triggering condition is then proposed for each module, which only uses local information to trigger data via the communication channel. In this way, the information exchange between subsystems can be reduced significantly compared to time‐triggered conventional control approaches, while the asymptotic stability of the closed‐loop is maintained. In contrast to the reported event‐triggered MPC results, the optimized controller is calculated based on state feedback control law for individual subsystems, which minimizes the upper limit on the infinite horizon cost function subject to constraints on the control inputs. The validness of the proposed scheme is demonstrated by simulation results.