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An observer‐based output feedback robust MPC approach for constrained LPV systems with bounded disturbance and noise
Author(s) -
Ping Xubin,
Yang Sen,
Wang Peng,
Li Zhiwu
Publication year - 2019
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4836
Subject(s) - control theory (sociology) , bounded function , convex optimization , robustness (evolution) , optimization problem , computer science , noise (video) , observer (physics) , controller (irrigation) , robust control , invariant (physics) , mathematical optimization , regular polygon , mathematics , control system , control (management) , engineering , artificial intelligence , image (mathematics) , mathematical analysis , chemistry , biology , biochemistry , geometry , quantum mechanics , agronomy , physics , electrical engineering , gene , mathematical physics
Summary The present paper addresses an observer‐based output feedback robust model predictive control for the linear parameter varying system with bounded disturbance and noise subject to input and state constraints. The main contribution is that the on‐line convex optimization problem not only simultaneously optimizes the observer and controller gains to stabilize the augmented closed‐loop system but also incorporates the refreshment of bounds of the estimation error set. The optimization problem steers the nominal augmented closed‐loop system to converge to the origin, and the real augmented closed‐loop system bounded within robust positive invariant set converges to a neighborhood of the origin such that recursive feasibility of the optimization and robust stability of the controlled system are ensured. Two numerical examples are given to illustrate the effectiveness of the method.