Stochastic Linear Quadratic Optimal Control with Indefinite Control Weights and Constraint for Discrete-Time Systems
Keyword(s):
The Cost
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The Karush-Kuhn-Tucker (KKT) theorem is used to study stochastic linear quadratic optimal control with terminal constraint for discrete-time systems, allowing the control weighting matrices in the cost to be indefinite. A generalized difference Riccati equation is derived, which is different from those without constraint case. It is proved that the well-posedness and the attainability of stochastic linear quadratic optimal control problem are equivalent. Moreover, an optimal control can be denoted by the solution of the generalized difference Riccati equation.
2014 ◽
Vol 228
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pp. 264-270
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1992 ◽
Vol 13
(3)
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pp. 227-245
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2012 ◽
Vol 34
(5)
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pp. 505-516
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1989 ◽
Vol 25
(11)
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pp. 1245-1247
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