Robust stabilization design for nonlinear stochastic system with Poisson noise via fuzzy interpolation method

Chen, Wen‐Hao; Chen, Bor‐Sen

doi:10.1016/j.fss.2012.10.014

Cited by 31 publications

(26 citation statements)

References 33 publications

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“…In this paper, the H − /H ∞ FDF design for Itô-type affine nonlinear stochastic systems and quasilinear systems have been discussed, and sufficient conditions for the existence of the desired FDF have been given via HJIs and LMIs, respectively. The key to the FDF design of affine nonlinear stochastic systems is how to solve the coupled HJIs, this is a very challenging problem, and some potential effective approaches to overcome this difficulty may refer to [8,25] for global linearization method, [1] for neural network method, and [9] for fuzzy interpolation method.…”

Section: Resultsmentioning

confidence: 99%

Fault detection filtering for Itô‐type affine nonlinear stochastic systems

Zhang

Deng

Zhang

et al. 2019

Asian Journal of Control

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This paper studies the mixed H − /H ∞ fault detection filtering of Itô-type nonlinear stochastic systems. Mixed H − /H ∞ filtering combines the system robustness to the external disturbance and the sensitivity to the fault of the residual signal. Firstly, for Itô-type affine nonlinear stochastic systems, some sufficient criteria are obtained for the existence of H − /H ∞ filter in terms of Hamilton-Jacobi inequalities (HJIs). Secondly, for a class of quasi-linear Itô systems, a sufficient condition is given for the existence of H − /H ∞ filter by means of linear matrix inequalities (LMIs). Finally, a numerical example is presented to illustrate the effectiveness of the proposed results.

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Section: Resultsmentioning

confidence: 99%

Fault detection filtering for Itô‐type affine nonlinear stochastic systems

Zhang

Deng

Zhang

et al. 2019

Asian Journal of Control

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“…By the similar procedure of completing the square in the proof of Theorem 2.1 of [17], it is easy to prove (i) of this theorem and EL u=u * ,v=0 V (y(U, t)) < 0 so that (ii) of this theorem can also be true. 2…”

Section: Proofmentioning

confidence: 95%

“…And the infinitesimal operator L for the system (2.3) is denoted as [17,25].) When there is only one mark in Θ,…”

Section: A General H ∞ State Feedback Control Setting For Nonlinear Smentioning

confidence: 99%

Robust control design for nonlinear stochastic partial differential systems with Poisson noise: Fuzzy implementation

Chen

Zhang

2014

Fuzzy Sets and Systems

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“…In recent years, the fuzzy approach has been widely used in many fields see [7], [8], [9], [10], [11], [23] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Robust control design of semilinear parabolic partial differential systems: A fuzzy approach

Gaye

Pagès

2014

2014 IEEE Conference on Control Applications (CCA)

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This communication deals with the robust H∞ stabilization of the semilinear partial differential system using Lyapunov theory. The time derivative of this Lyapunov function can be made strictly negative definite by an appropriate choice of controls. In general, it is difficult to control partial differential systems. In order to simplify the design procedure of control law, a fuzzy partial differential system based on fuzzy interpolation approach is proposed. Based on this distributed model, the distributed robust control design is proposed to attenuate disturbances via solving linear matrix inequalities (LMIs). Finally, numerical results are presented and discussed to illustrate the effectiveness of the proposed approach.

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Robust stabilization design for nonlinear stochastic system with Poisson noise via fuzzy interpolation method

Cited by 31 publications

References 33 publications

Fault detection filtering for Itô‐type affine nonlinear stochastic systems

Fault detection filtering for Itô‐type affine nonlinear stochastic systems

Robust control design for nonlinear stochastic partial differential systems with Poisson noise: Fuzzy implementation

Robust control design of semilinear parabolic partial differential systems: A fuzzy approach

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