Open AccessOutput feedback model predictive control for Hammerstein model with bounded disturbance
Author(s) -
Ding Baocang,
Wang Jun,
Su Benji
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/cth2.12283
Subject(s) - control theory (sociology) , nonlinear system , parameterized complexity , bounded function , model predictive control , controller (irrigation) , mathematics , stability (learning theory) , inverse , state (computer science) , computer science , control (management) , algorithm , artificial intelligence , mathematical analysis , physics , geometry , quantum mechanics , machine learning , agronomy , biology
This paper proposes two model predictive control (MPC) methods for a Hammerstein model with both unmeasurable state and bounded disturbance. First, a dynamic output feedback MPC is designed such that the dynamics of the state estimation error is decoupled from that of the estimated state. Hence, the separation principle holds and a part of the controller parameters can be designed off‐line. Second, a case is considered where Hammerstein nonlinearity is not exactly inverted, that is, a reserved static nonlinearity exists in the closed‐loop system. This reserved nonlinearity is merged into a polytopic description in combination with the linear dynamic part. The control move is then parameterized as a feedback law followed by the approximate inverse of Hammerstein nonlinearity. The recursive feasibility and closed‐loop stability of both approaches are guaranteed. Numerical examples are given in order to show effectiveness of the proposed approaches.