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Synthesis of Mixed Objective Output Feedback Robust Model Predictive Control
Author(s) -
Jiang Wei,
Wang Hongli,
Lu Jinghui,
Qin Weiwei,
Cai Guangbin
Publication year - 2017
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1494
Subject(s) - control theory (sociology) , linear matrix inequality , convex optimization , model predictive control , robust control , output feedback , scheme (mathematics) , bounded function , computer science , controller (irrigation) , observer (physics) , stability (learning theory) , mathematical optimization , regular polygon , control (management) , mathematics , control system , engineering , mathematical analysis , agronomy , physics , geometry , quantum mechanics , artificial intelligence , machine learning , electrical engineering , biology
Aiming at the constrained polytopic uncertain system with energy‐bounded disturbance and unmeasurable states, a novel synthesis scheme to design the output feedback robust model predictive control(MPC)is put forward by using mixed H 2 /H ∞ design approach. The proposed scheme involves an offline design of a robust state observer using linear matrix inequalities(LMIs)and an online output feedback robust MPC algorithm using the estimated states in which the desired mixed objective robust output feedback controllers are cast into efficiently tractable LMI‐based convex optimization problems. In addition, the closed‐loop stability and the recursive feasibility of the proposed robust MPC are guaranteed through an appropriate reformulation of the estimation error bound (EEB). A numerical example subject to input constraints illustrates the effectiveness of the proposed controller.