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Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

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  • Huanqing Wang
  • Qi Zhou
  • Xuebo Yang
  • Hamid Reza Karimi

Abstract

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

Suggested Citation

  • Huanqing Wang & Qi Zhou & Xuebo Yang & Hamid Reza Karimi, 2014. "Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, March.
    • Handle: RePEc:hin:jnlmpe:841306
    DOI: 10.1155/2014/841306
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    Cited by:

    1. Yi-Jen Mon, 2022. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology," Sustainability, MDPI, vol. 14(9), pages 1-14, April.

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