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Model predictive control for LPV models with maximal stabilizable model range
Author(s) -
Yang Yuanqing,
Ding Baocang
Publication year - 2020
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.2070
Subject(s) - parameterized complexity , control theory (sociology) , model predictive control , range (aeronautics) , lyapunov function , convex optimization , mathematics , controller (irrigation) , stability (learning theory) , regular polygon , control (management) , mathematical optimization , computer science , nonlinear system , engineering , algorithm , artificial intelligence , agronomy , physics , geometry , quantum mechanics , machine learning , biology , aerospace engineering
This paper characterizes model predictive control (MPC) for linear parameter varying (LPV) models subject to state and input constraints, which is based on the homogeneous polynomially parameterized (HPP) Lyapunov function and HPP control law with tunable complexity degrees. The controller guarantees the closed‐loop asymptotic stability and finds the control move through the convex optimization. While it is known that this technique can improve the control performance and reduce conservatism, we suggest that it also enlarges, and maximizes with the sufficiently large complexity degrees, the stabilizable LPV model range. The computational burden becomes heavier when the complexity degrees increase. However, the main contributions of this paper are more on theory than on practice. It explores to what extent robust MPC can be applied for stabilization of LPV models. Numerical examples are provided to illustrate the effectiveness of the proposed technique.