Infinite-time stochastic linear quadratic optimal control for unknown discrete-time systems using adaptive dynamic programming approach

2016 ◽  
Vol 171 ◽  
pp. 379-386 ◽  
Author(s):  
Tao Wang ◽  
Huaguang Zhang ◽  
Yanhong Luo
Keyword(s):  
Optimal Control ◽  
Adaptive Dynamic Programming ◽  
Programming Approach ◽  
Linear Quadratic ◽  
Infinite Time ◽  
Linear Quadratic Optimal Control ◽  
Dynamic Programming Approach ◽  
Discrete Time Systems ◽  
Quadratic Optimal Control ◽  
Time Systems

scholarly journals Stochastic Linear Quadratic Optimal Control with Indefinite Control Weights and Constraint for Discrete-Time Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Xikui Liu ◽  
Guiling Li ◽  
Yan Li
Keyword(s):  
Optimal Control ◽  
Riccati Equation ◽  
Discrete Time ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Discrete Time Systems ◽  
Quadratic Optimal Control ◽  
The Cost ◽  
Time Systems ◽  
Difference Riccati Equation

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.

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