Paper Info
Title
Data-driven partially observable dynamic processes using adaptive dynamic programming
Abstract
Adaptive dynamic programming (ADP) has been widely recognized as one of the “core methodologies” to achieve optimal control for intelligent systems in Markov decision process (MDP). Generally, ADP control design requires all the information of the system dynamics. However, in many practical situations, the measured input and output data can only represent part of the system states. This means the complete information of the system cannot be available in many real-world cases, which narrows the range of application of the ADP design. In this paper, we propose a data-driven ADP method to stabilize the system with partially observable dynamics based on neural network techniques. A state network is integrated into the typical actor-critic architecture to provide an estimated state from the measured input/output sequences. The theoretical analysis and the stability discussion of this data-driven ADP method are also provided. Two examples are studied to verify our proposed method.
Year
DOI
Venue
2014
10.1109/ADPRL.2014.7010628
ADPRL
Keywords
Field
DocType
adaptive dynamic programming,optimal control,control design,actor-critic architecture,neurocontrollers,control system synthesis,core methodologies,data-driven partially observable dynamic process,adp,intelligent systems,markov decision process,dynamic programming,system dynamics,mdp,markov processes,neural network techniques
Dynamic programming,Optimal control,Intelligent decision support system,Computer science,Control theory,Partially observable Markov decision process,Markov decision process,Input/output,System dynamics,Artificial neural network
Conference
Citations 
PageRank 
References 
3
0.41
20
Authors
4
Name
Order
Citations
PageRank
Xiangnan Zhong134616.35
Zhen Ni252533.47
Yufei Tang320322.83
Haibo He43653213.96