Premium
An optimal approach to output‐feedback robust model predictive control of LPV systems with disturbances
Author(s) -
Yang Weilin,
Gao Jianwei,
Feng Gang,
Zhang TieJun
Publication year - 2016
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.3505
Subject(s) - control theory (sociology) , model predictive control , bounded function , robust control , ellipsoid , observer (physics) , computation , computer science , norm (philosophy) , convex optimization , mathematics , quadratic equation , mathematical optimization , regular polygon , control (management) , control system , algorithm , engineering , artificial intelligence , electrical engineering , mathematical analysis , physics , geometry , quantum mechanics , astronomy , law , political science
Summary An observer‐based output feedback predictive control approach is proposed for linear parameter varying systems with norm‐bounded external disturbances. Sufficient and necessary robust positively invariant set conditions of the state estimation error are developed to determine the minimal ellipsoidal robust positively invariant set and observer gain through offline computation. The quadratic upper bound of state estimation error is updated and included in anℋ ∞ ‐type cost function of predictive control to optimize transient output feedback control performance. Recursive feasibility of the dynamic convex optimization problem is guaranteed in the proposed predictive control strategy. With the input‐to‐state stable observer, the closed‐loop control system states are steered into a bounded set. Simulation results are given to demonstrate the effectiveness of the proposed control strategy. Copyright © 2016 John Wiley & Sons, Ltd.