Dynamic surface control for non-strict-feedback stochastic nonlinear interconnected systems

In this paper, a neural-network-based dynamic surface control method is developed for a class of non-strict-feedback stochastic nonlinear interconnected systems. Neural-networks (NNS) combined with adaptive backstepping technique are applied to model the unknown nonlinear functions of the stochastic...

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Bibliographic Details
Published in2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS) pp. 693 - 698
Main Authors Huan Li, Ben Niu, Yakun Su, Guangju Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Adaptive systems
Interconnected systems
Observers
Power system dynamics
Stochastic processes
Vehicle dynamics
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Summary:In this paper, a neural-network-based dynamic surface control method is developed for a class of non-strict-feedback stochastic nonlinear interconnected systems. Neural-networks (NNS) combined with adaptive backstepping technique are applied to model the unknown nonlinear functions of the stochastic interconnected system. The dynamic surface control (DSC) method is adopted to ensure the computation burden is greatly reduced. The proposed controllers guarantee the closed-loop interconnected stochastic nonlinear system is globally bounded stable in probability.
DOI:10.1109/ICCSS.2017.8091503