Title
SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays.
Abstract
In this paper, a novel data-driven single neuron predictive control strategy is proposed for non-Gaussian networked control systems with metrology delays in the information theory framework. Firstly, survival information potential (SIP), instead of minimum entropy, is used to formulate the performance index to characterize the randomness of the considered systems, which is calculated by oversampling method. Then the minimum values can be computed by optimizing the SIP-based performance index. Finally, the proposed strategy, minimum entropy method and mean square error (MSE) are applied to a networked motor control system, and results demonstrated the effectiveness of the proposed strategy.
Year
DOI
Venue
2018
10.3390/e20070494
ENTROPY
Keywords
Field
DocType
networked control systems,data-driven predictive strategy,non-Gaussian random delays,non-Gaussian disturbances,survival information potential
Mathematical optimization,Control theory,Metrology,Model predictive control,Gaussian,Control system,Mathematics
Journal
Volume
Issue
ISSN
20
7
1099-4300
Citations 
PageRank 
References 
0
0.34
15
Authors
5
Name
Order
Citations
PageRank
Xinying Xu112914.79
Yalan Zhao200.34
Mifeng Ren3167.85
Lan Cheng411.37
Mingyue Gong500.34