Dynamic Programming Method for Constrained Discrete-Time Optimal Control

1999 ◽  
Vol 101 (2) ◽  
pp. 259-283 ◽  
Author(s):  
C. R. Dohrmann ◽  
R. D. Robinett
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Discrete Time ◽  
Programming Method ◽  
Time Optimal Control ◽  
Dynamic Programming Method ◽  
Time Optimal

scholarly journals Neural Network-Based Intelligent Computing Algorithms for Discrete-Time Optimal Control with the Application to a Cyberphysical Power System

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Feng Jiang ◽  
Kai Zhang ◽  
Jinjing Hu ◽  
Shunjiang Wang
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Power System ◽  
Discrete Time ◽  
Adaptive Dynamic Programming ◽  
Time Optimal Control ◽  
Value Iteration ◽  
Optimal Control Strategy ◽  
Bellman Equations ◽  
Time Optimal

Adaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration (PI) and value iteration (VI) are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming (HDP) method is introduced, which avoids the requirement of initial admissible control and achieves fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system.

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