Open AccessModel predictive control of switching continuous‐time systems with stochastic jumps: Application to an electric current source
Author(s) -
Vargas Alessandro N.,
Ishihara João Y.,
Caruntu Constantin F.,
Zhang Lixian,
Djanan Armand A. Nanha
Publication year - 2022
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/cth2.12242
Subject(s) - model predictive control , control theory (sociology) , computer science , control (management) , controller (irrigation) , process (computing) , state (computer science) , extension (predicate logic) , current (fluid) , control engineering , engineering , algorithm , artificial intelligence , electrical engineering , agronomy , biology , programming language , operating system
This paper proposes an extension of the model predictive control framework for switching continuous‐time linear systems. The switching times follow a stochastic process with limited statistical information. At each switching time, the controller knows the system state, but it is blind with respect to the switching continuous‐time subsystems. In this setting, the paper's main contribution is to show how to compute the model predictive control gain. The paper also illustrates the implications of our approach for applications. The approach was used in practice to control an electric current source that supplied a switching load. The experimental data support the usefulness of our approach.