Some Properties of the Dual Adaptive Stochastic Control Algorithm.

1979 ◽  
Author(s):  
Pierre Dersin ◽  
Michael Athans ◽  
David A. Kendrick
Keyword(s):  
Stochastic Control ◽  
Control Algorithm ◽  
Adaptive Stochastic Control

scholarly journals Adaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence method

2019 ◽  
Vol 67 (4) ◽  
pp. 314-321
Author(s):  
Tomas Kozel ◽  
Milos Stary
Keyword(s):  
Artificial Intelligence ◽  
Stochastic Control ◽  
Control Process ◽  
Fuzzy Model ◽  
Water Reservoir ◽  
Control Algorithms ◽  
Adaptive Models ◽  
Forecast Data ◽  
Adaptive Stochastic Control ◽  
Open Reservoir

Abstract The design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods (learning fuzzy model and neural networks) are described in this article. This procedure was tested on an artificial reservoir. Reservoir parameters have been designed to cause critical disturbances during the control process, and therefore the influences of control algorithms can be demonstrated in the course of controlled outflow of water from the reservoir. The results of the stochastic adaptive models were compared. Further, stochastic model results were compared with a resultant course of management obtained using the method of classical optimisation (differential evolution), which used stochastic forecast data from real series (100% forecast). Finally, the results of the dispatcher graph and adaptive stochastic control were compared. Achieved results of adaptive stochastic management provide inspiration for continuing research in the field.

scholarly journals OPTCON3: An Active Learning Control Algorithm for Nonlinear Quadratic Stochastic Problems

2019 ◽  
Vol 56 (1) ◽  
pp. 145-162
Author(s):  
V. Blueschke-Nikolaeva ◽  
D. Blueschke ◽  
R. Neck
Keyword(s):  
Active Learning ◽  
Stochastic Control ◽  
Control Algorithm ◽  
Dynamic Models ◽  
Control Problems ◽  
Dual Effect ◽  
Policy Problem ◽  
Optimal Policies ◽  
Nonlinear Dynamic Models ◽  
Simple Economic

AbstractIn this paper, we describe the new OPTCON3 algorithm, which serves to determine approximately optimal policies for stochastic control problems with a quadratic objective function and nonlinear dynamic models. It includes active learning and the dual effect of optimizing policies, whereby optimal policies are used to learn about the stochastics of the dynamic system in addition to their immediate effect on the performance of the system. The OPTCON3 algorithm approximates the nonlinear model with a time-varying linear model and applies a procedure similar to that of Kendrick to the series of linearized models to calculate approximately optimal policies. The results for two simple economic models serve to test the OPTCON3 algorithm and compare it to previous solutions of the stochastic control problem. Initial evaluations show that the OPTCON3 approach may be promising to enhance our understanding of the adaptive economic policy problem under uncertainty.

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