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Persistent disturbance rejection via state feedback for networked control systems

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  • Yue, Dong
  • Lam, James
  • Wang, Zidong

Abstract

The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.

Suggested Citation

  • Yue, Dong & Lam, James & Wang, Zidong, 2009. "Persistent disturbance rejection via state feedback for networked control systems," Chaos, Solitons & Fractals, Elsevier, vol. 40(1), pages 382-391.
  • Handle: RePEc:eee:chsofr:v:40:y:2009:i:1:p:382-391
    DOI: 10.1016/j.chaos.2007.07.073
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    References listed on IDEAS

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    1. Lien, Chang-Hua, 2007. "H∞ non-fragile observer-based controls of dynamical systems via LMI optimization approach," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 428-436.
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    3. Yan, Huaicheng & Huang, Xinhan & Wang, Min & Zhang, Hao, 2007. "Delay-dependent stability criteria for a class of networked control systems with multi-input and multi-output," Chaos, Solitons & Fractals, Elsevier, vol. 34(3), pages 997-1005.
    4. Wei, Guoliang & Shu, Huisheng, 2007. "H∞ filtering on nonlinear stochastic systems with delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(2), pages 663-670.
    5. Gao, Huijun & Lam, James & Wang, Zidong, 2007. "Discrete bilinear stochastic systems with time-varying delay: Stability analysis and control synthesis," Chaos, Solitons & Fractals, Elsevier, vol. 34(2), pages 394-404.
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