Stochastic Modeling and Optimization for Robust Power Management in a Partially Observable System

2007 ◽  
Author(s):  
Qinru Qiu ◽  
Ying Tan ◽  
Qing Wu
Keyword(s):  
Power Management ◽  
Stochastic Modeling ◽  
Modeling And Optimization ◽  
Observable System ◽  
Partially Observable System ◽  
Partially Observable

Filtering method for linear and non-linear stochastic optimal control of partially observable systems

Filomat ◽  
2017 ◽  
Vol 31 (19) ◽  
pp. 5979-5992 ◽  
Author(s):  
A. Delavarkhalafi ◽  
A. Poursherafatan
Keyword(s):  
Optimal Control ◽  
Stochastic Optimal Control ◽  
Heuristic Method ◽  
Control Factor ◽  
Filtering Method ◽  
Observable System ◽  
State Process ◽  
Non Linear ◽  
Partially Observable System ◽  
Partially Observable

This paper studies two linear methods for linear and non-linear stochastic optimal control of partially observable problem (SOCPP). At first, it introduces the general form of a SOCPP and states it as a functional matrix. A SOCPP has a payoff function which should be minimized. It also has two dynamic processes: state and observation. In this study, it is presented a deterministic method to find the control factor which has named feedback control and stated a modified complete proof of control optimality in a general SOCPP. After finding the optimal control factor, it should be substituted in the state process to make the partially observable system. Next, it introduces a linear filtering method to solve the related partially observable system with complete details. Finally, it is presented a heuristic method in discrete form for estimating non-linear SOCPPs and it is stated some examples to evaluate the performance of introducing methods.

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