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A robust model predictive control algorithm for incrementally conic uncertain/nonlinear systems
Author(s) -
Açıkmeşe Behçet,
Carson John M.,
Bayard David S.
Publication year - 2011
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.1613
Subject(s) - control theory (sociology) , nonlinear system , model predictive control , feed forward , conic section , bounded function , computer science , trajectory , robust control , controller (irrigation) , optimal control , mathematics , mathematical optimization , control (management) , control engineering , engineering , artificial intelligence , mathematical analysis , agronomy , physics , geometry , quantum mechanics , astronomy , biology
Abstract This paper presents a robustly stabilizing model predictive control algorithm for systems with incrementally conic uncertain/nonlinear terms and bounded disturbances. The resulting control input consists of feedforward and feedback components. The feedforward control generates a nominal trajectory from online solution of a finite‐horizon constrained optimal control problem for a nominal system model. The feedback control policy is designed off‐line by utilizing a model of the uncertainty/nonlinearity and establishes invariant ‘state tubes’ around the nominal system trajectories. The entire controller is shown to be robustly stabilizing with a region of attraction composed of the initial states for which the finite‐horizon constrained optimal control problem is feasible for the nominal system. Synthesis of the feedback control policy involves solution of linear matrix inequalities. An illustrative numerical example is provided to demonstrate the control design and the resulting closed‐loop system performance. Copyright © 2010 John Wiley & Sons, Ltd.