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Neural Computing and Applications

Erschienen in:

01.10.2013 | Original Article

Intelligent control of nonlinear systems with application to chemical reactor recycle

verfasst von: Dong-Juan Li, Jing Zhang, Yang Cui, Lei Liu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2013

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Abstract

The dynamic surface control technique can simplify the backstepping design for the control of nonlinear systems by overcoming the problem of “explosion of complexity.” In this paper, we incorporate this design technique into a neural network-based adaptive control design framework for a class of nonlinear stochastic systems. The time delays exist in the gain of the stochastic disturbance in the systems, and the neural networks are employed to compensate for all unknown nonlinear terms depending on the delayed output. The proposed approach is able to eliminate the problem of “explosion of complexity” inherent in the existing method. It can be proven that all the signals are semi-globally uniformly ultimately bounded in probability, and the system output tracks the reference signal to a bounded compact set. A simulation example is given to verify the effectiveness of the proposed approach.

Vorheriger Artikel Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties

Nächster Artikel Robust and smart spectral clustering from normalized cut

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Titel: Intelligent control of nonlinear systems with application to chemical reactor recycle
verfasst von: Dong-Juan Li
Jing Zhang
Yang Cui
Lei Liu
Publikationsdatum: 01.10.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1100-5

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