Title
Single Neuron Stochastic Predictive PID Control Algorithm for Nonlinear and Non-Gaussian Systems Using the Survival Information Potential Criterion.
Abstract
This paper presents a novel stochastic predictive tracking control strategy for nonlinear and non-Gaussian stochastic systems based on the single neuron controller structure in the framework of information theory. Firstly, in order to characterize the randomness of the control system, survival information potential (SIP), instead of entropy, is adopted to formulate the performance index, which is not shift-invariant, i.e., its value varies with the change of the distribution location. Then, the optimal weights of the single neuron controller can be obtained by minimizing the presented SIP based predictive control criterion. Furthermore, mean-square convergence of the proposed control algorithm is also analyzed from the energy conservation perspective. Finally, a numerical example is given to show the effectiveness of the proposed method.
Year
DOI
Venue
2016
10.3390/e18060218
ENTROPY
Keywords
Field
DocType
nonlinear and non-Gaussian systems,single neuron controller,stochastic predictive control,survival information potential criterion,mean square convergence
Information theory,Convergence (routing),Control theory,Mathematical optimization,Nonlinear system,Control theory,Computer science,Model predictive control,Gaussian,Control system,Randomness
Journal
Volume
Issue
Citations 
18
6
1
PageRank 
References 
Authors
0.36
18
5
Name
Order
Citations
PageRank
Mifeng Ren1167.85
Ting Cheng210.36
Jung-hui Chen3338.60
Xinying Xu412914.79
Lan Cheng511.37