Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm View Full Text


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Article Info

DATE

2017-04-19

AUTHORS

Imen Zaidi, Mohamed Chtourou, Mohamed Djemel

ABSTRACT

This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy. More... »

PAGES

213-225

References to SciGraph publications

  • 2008-01-01. Robust MCD-Based Backpropagation Learning Algorithm in ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING – ICAISC 2008
  • 1998-12. Adaptive sliding mode approach for learning in a feedforward neural network in NEURAL COMPUTING AND APPLICATIONS
  • 2010. Fast Robust Learning Algorithm Dedicated to LMLS Criterion in ARTIFICAL INTELLIGENCE AND SOFT COMPUTING
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    http://scigraph.springernature.com/pub.10.1007/s11633-017-1062-2

    DOI

    http://dx.doi.org/10.1007/s11633-017-1062-2

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