Event-Triggered Fuzzy Filtering for Nonlinear Dynamic Systems via Reduced-Order Approach

“…Many nonlinear systems are multi-input and multi-output systems [33][34][35][36][37] , which the system models are often complex and diverse, how to design simple controllers/filters to meet the corresponding needs. Recently, Su et al [38] proposed a reduced-order filter (ROF) design method, namely, the order of the ROF is lower than the original plant, which will be more favorable for the real-time filtering procedure because some redundancy and extra calculation can be effectively avoided by this filter [39,40] . However, as far as the author knows, there is little work on the design of fuzzy ROF for PDE systems, which arouses the author' s interest.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy reduced-order filtering for nonlinear parabolic PDE systems with limited communication

Zhang¹,

Song²,

Li³

et al. 2021

CES

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This paper investigates a fuzzy reduced-order filter design for a class of nonlinear partial differential equation (PDE) systems. First, a Takagi-Sugeno (T-S) fuzzy model is considered to reconstruct the nonlinear PDE system. Then, the employment of an event-triggered mechanism (ETM) can effectively avoid signal redundancy and improve network resource utilization. Furthermore, based on the advantages of the fuzzy model and ETM, several Lyapunov functions are designed and the proposed filter parameters are obtained by adopting linear matrix inequality methods to satisfy the asymptotic stability condition with H∞ performance. Finally, a simulation example is presented to demonstrate the practicality and effectiveness of the proposed filter design method.

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“…It's notable that the Takagi‐Sugeno (T‐S) fuzzy model 5 is one of the most commonly used fuzzy models, for example, nonlinear control synthesis via state/output‐feedback control 6‐8 . Meanwhile, T‐S model‐based fuzzy estimation has also been addressed on the topic of nonlinear filtering, 9‐11 nonlinear fault estimation (FE), 12,13 and so on. Nevertheless, it may be pointed that these reported results would be restricted due to the appliance of the so‐called common Lyapunov functions (LFs) while their derived conditions must be very conservative 14 .…”

Section: Introductionmentioning

confidence: 99%

Robust fault estimation of discrete‐time nonlinear plants via a comprehensive partition‐based switching scheme

Xie

Zang

Yue

et al. 2020

Intl J Robust & Nonlinear

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The problem of robust fault estimation (FE) for a class of nonlinear systems in discrete-time domain is studied via proposing a comprehensive partition-based switching scheme. Both enhancement factors and attenuation factors are designed synchronously for accomplishing a comprehensive partition of the entire space spanned by the considered normalized fuzzy weighting functions. Thanks to the proposed comprehensive partition-based switching scheme, six subspaces would be obtained by identifying the real-time permutation order of the underlying parameters. Therefore, a novel fuzzy FE observer with six kinds of switching modes can be on-line implemented. As a result, the robust H ∞ performance index can be enhanced since much more information is employed than those reported results. Finally, simulation comparisons over the existing results are provided to show the obtained advantages of the article.

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Event-Triggered Fuzzy Filtering for Nonlinear Dynamic Systems via Reduced-Order Approach

Cited by 92 publications

References 31 publications

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

Hybrid Event-Triggered Filtering for Nonlinear Markov Jump Systems With Stochastic Cyber-Attacks

Fuzzy reduced-order filtering for nonlinear parabolic PDE systems with limited communication

Robust fault estimation of discrete‐time nonlinear plants via a comprehensive partition‐based switching scheme

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