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Quadratic boundedness of LPV systems via saturated dynamic output feedback controller
Author(s) -
Ping Xubin,
Wang Peng,
Li Zhiwu
Publication year - 2017
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2328
Subject(s) - control theory (sociology) , convex optimization , optimization problem , linearization , mathematical optimization , controller (irrigation) , scheduling (production processes) , linear system , linear matrix inequality , mathematics , computer science , regular polygon , nonlinear system , control (management) , mathematical analysis , physics , geometry , quantum mechanics , artificial intelligence , agronomy , biology
Summary For the linear parameter varying systems with bounded disturbance, a saturated dynamic output feedback controller is designed by specifically considering input saturation, to stabilize the closed‐loop system. The controller parameters and the corresponding region of attraction are calculated by solving an off‐line optimization problem with respect to input saturation, state constraints, and robust stability. In the off‐line optimization problem, both the unknown and available scheduling parameters are considered for the linear parameter varying systems. When the unknown scheduling parameters are considered, the off‐line optimization problem is nonconvex and can be solved by the cone complementary linearization method. When the available scheduling parameters are considered, the off‐line optimization problem can be reformulated as convex optimization due to the parameter dependent form of controller parameters. In the both cases, input saturation is specifically handled by introducing a set of linear matrix inequalities into the off‐line optimization problem, which can reduce the conservatism of the controller design and fully exploit the controller capability. Based on the real‐time estimated state, system output, and scheduling parameters, the actual input can be obtained by saturating the dynamic output feedback controller, and steer the augmented state quickly converge to the neighborhood of the origin. Two numerical examples are provided to illustrate the proposed approaches.