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Entropy 2018, 20(7), 494; https://doi.org/10.3390/e20070494

SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays

College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China
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Received: 21 May 2018 / Revised: 18 June 2018 / Accepted: 22 June 2018 / Published: 26 June 2018
(This article belongs to the Section Information Theory, Probability and Statistics)
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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. View Full-Text
Keywords: networked control systems; data-driven predictive strategy; non-Gaussian random delays; non-Gaussian disturbances; survival information potential networked control systems; data-driven predictive strategy; non-Gaussian random delays; non-Gaussian disturbances; survival information potential
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, X.; Zhao, Y.; Ren, M.; Cheng, L.; Gong, M. SIP-Based Single Neuron Stochastic Predictive Control for Non-Gaussian Networked Control Systems with Uncertain Metrology Delays. Entropy 2018, 20, 494.

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