Output‐feedback model predictive control for stochastic systems with multiplicative and additive uncertainty

Summary This paper studies the output‐feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off‐line optimization algorithm to optimize feedback gains of the observer and the dual‐mode control policy. After...

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Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 28; no. 1; pp. 86 - 102
Main Authors Li, Jiwei, Li, Dewei, Xi, Yugeng, Xu, Yuli, Gan, Zhongxue
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 10.01.2018
Subjects
Algorithms
Control systems
Linear systems
Lower bounds
Mathematical models
model predictive control
Output feedback
Predictive control
probabilistic constraints
quadratic programming
Stochastic systems
Trajectory analysis
Uncertainty
Wind turbines
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Summary:Summary This paper studies the output‐feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off‐line optimization algorithm to optimize feedback gains of the observer and the dual‐mode control policy. After that, by defining a cuboid tube whose center and boundary are both time‐varying variables, we develop a set sequence with increased freedom to contain stochastic system trajectories. A quadratic performance function with analytic upper and lower bounds is minimized such that it decreases exponentially to a finite range under the expectation. The resulting MPC algorithms are proved to guarantee practically stochastic input‐to‐state stability. A numerical example of the wind turbine model illustrates the properties of the MPC algorithms.
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.3856