sampled‐data fuzzy control for non‐linear parabolic distributed parameter systems with control inputs missing

Wang, Zipeng; Wu, Huai‐Ning

doi:10.1049/iet-cta.2016.0724

IET Control Theory & Applications

2017

DOI: 10.1049/iet-cta.2016.0724

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sampled‐data fuzzy control for non‐linear parabolic distributed parameter systems with control inputs missing

Zipeng Wang

Huai‐Ning Wu

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Cited by 12 publications

References 54 publications

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H_∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

Zhang

2019

Intl J Robust & Nonlinear

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Summary This paper addresses the problem of H∞ boundary control for a class of nonlinear stochastic distributed parameter systems expressed by parabolic stochastic partial differential equations (SPDEs) of Itô type. A simple but effective H∞ boundary static output feedback (SOF) control scheme with collocated boundary measurement is introduced to ensure the local exponential stability in the mean square sense with an H∞ performance. By using the semigroup theory, the disturbance‐free closed‐loop well‐posedness analysis is first given. Then, based on the SPDE model, a general linear matrix inequality based H∞ boundary SOF control design is provided via Lyapunov technique and infinite‐dimensional infinitesimal operator, such that the disturbance‐free closed‐loop system is locally exponentially stable in the mean square sense and the H∞ performance of disturbance attenuation can also be achieved in the presence of disturbances. Finally, simulation results on a stochastic Fisher‐Kolmogorov‐Petrovsky‐Piscounov equation illustrate the effectiveness of the proposed method.

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H_∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

Zhang

2019

Intl J Robust & Nonlinear

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Robust Fuzzy Filter Design for Nonlinear Parabolic Partial Differential Systems with Continuous Wiener Noise and Discontinuous Poisson Noise

Chen

Lee

Chen

2018

Int. J. Fuzzy Syst.

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Online distributed fuzzy modeling of nonlinear PDE systems: Computation based on adaptive algorithms

Mardani

Shasadeghi

Safarinejadian

et al. 2019

Applied Soft Computing

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With the emergence of novel model-based controllers for partial differential equation (PDE) systems, identifying the mathematical model of PDE systems has become a promising and complicated research topic. This paper suggests a new method to identify an adaptive Takagi-Sugeno (TS) fuzzy PDE model for nonlinear multi-input multi-output (MIMO) first-order PDE systems. The proposed approach is performed online based on the measured input and output data of the nonlinear PDE systems. Furthermore, the identification process will be obtained for the cases that the noise is either white or colored. For the case of white noise, a nonlinear recursive least square (NRLS) approach is applied to identify the nonlinear system. On the other hand, when the colored noise is exerted to the nonlinear PDE system, the fuzzy PDE model of the nonlinear PDE system and also nonlinear colored noise are identified based on the nonlinear extended matrix methods (NEMM). Moreover, the problem of identification for both colored and white noise cases is investigated when premise variables of membership functions are known or unknown. Finally, in order to illustrate the effectiveness and merits of the proposed methods, the identification method is applied to a practical nonisothermal Plug-Flow reactor (PFR) and a hyperbolic PDE system with Ltka-Volterra type applications. As it is expected, the evolutions of the error between the state variables for the obtained TS fuzzy PDE model and the output data converge to the zero in the steady-state conditions. Thus one concludes, the proposed identification algorithm can accurately adjust both consequents and antecedents parameters of TS fuzzy PDE model.

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sampled‐data fuzzy control for non‐linear parabolic distributed parameter systems with control inputs missing

Cited by 12 publications

References 54 publications

H_∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

H_∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

Robust Fuzzy Filter Design for Nonlinear Parabolic Partial Differential Systems with Continuous Wiener Noise and Discontinuous Poisson Noise

Online distributed fuzzy modeling of nonlinear PDE systems: Computation based on adaptive algorithms

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sampled‐data fuzzy control for non‐linear parabolic distributed parameter systems with control inputs missing

Cited by 12 publications

References 54 publications

H∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

H∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

Robust Fuzzy Filter Design for Nonlinear Parabolic Partial Differential Systems with Continuous Wiener Noise and Discontinuous Poisson Noise

Online distributed fuzzy modeling of nonlinear PDE systems: Computation based on adaptive algorithms

Contact Info

Product

Resources

About

H_∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems

H_∞ boundary control for a class of nonlinear stochastic parabolic distributed parameter systems