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Single Neuron Stochastic Predictive PID Control Algorithm for Nonlinear and Non-Gaussian Systems Using the Survival Information Potential Criterion

1
College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taoyuan 32023, Taiwan, Republic of China
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2016, 18(6), 218; https://doi.org/10.3390/e18060218
Received: 17 March 2016 / Revised: 14 May 2016 / Accepted: 26 May 2016 / Published: 3 June 2016
(This article belongs to the Section Information Theory, Probability and Statistics)
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. View Full-Text
Keywords: nonlinear and non-Gaussian systems; single neuron controller; stochastic predictive control; survival information potential criterion; mean square convergence nonlinear and non-Gaussian systems; single neuron controller; stochastic predictive control; survival information potential criterion; mean square convergence
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Ren, M.; Cheng, T.; Chen, J.; Xu, X.; Cheng, L. Single Neuron Stochastic Predictive PID Control Algorithm for Nonlinear and Non-Gaussian Systems Using the Survival Information Potential Criterion. Entropy 2016, 18, 218.

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