Fuzzy-Affine-Model-Based Sampled-Data Filtering Design for Stochastic Nonlinear Systems

Qiu, Jianbin; Ji, Wenqiang; Lam, Hak‐Keung; Wang, Meng

doi:10.1109/tfuzz.2020.3021699

Cited by 34 publications

(10 citation statements)

References 39 publications

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“…In addition, in view of many signal processing and control applications of simulation process are realized by digital computers, sampled-data filtering problem has attracted a lot of attention. 1,2 Therefore, in our future research, we will focus on generalizing the results of this article to nonlinear systems with stochastic disturbances under the sampling-data filtering framework.…”

Section: Discussionmentioning

confidence: 99%

“…It is well-known that stochastic disturbance often exists in many practical processes, such as economics, microelectronics, and biology. 1,2 Therefore, the study on nonlinear stochastic systems has attracted more and more attention. 3–6 Especially, the controller design of stochastic systems has become a research hotspot due to its important theoretical and practical values.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Zhu

Wang

Han

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, the problem of adaptive multi-dimensional Taylor network control for strict-feedback nonlinear stochastic systems with time-delay is investigated. To overcome the control degradation resulting from the delay terms, the appropriate integral-type Lyapunov–Krasovskii functions are introduced. A novel adaptive multi-dimensional Taylor network control scheme is provided via backstepping technique. The proposed adaptive multi-dimensional Taylor network controller can ensure that all signals in the closed-loop system are bounded in probability and the tracking error eventually converges to a small neighborhood of the origin. Three simulation examples are given to demonstrate the effectiveness of the proposed control scheme.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Zhu

Wang

Han

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The Takagi–Sugeno (T-S) fuzzy affine model is known to be very effective in dealing with such uncertainties and disturbances. As the state-of-the-art result in that field, a sampled-data filtering design technique for I

o stochastic T-S fuzzy affine system has been proposed recently [ 31 ].…”

Section: Pmf With Reliable Time Propagationmentioning

confidence: 99%

Reliable Time Propagation Algorithms for PMF and RBPMF

Sung

Lee

2021

Sensors

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This paper addresses the reliable time propagation algorithms for Point Mass Filter (PMF) and Rao–Blackwellized PMF (RBPMF) for the nonlinear estimaton problem. The conventional PMF and RBPMF process the probability diffusion for the time propagation with the direct sampled-values of the process noise. However, if the grid interval is not dense enough, it fails to represent the statistical characteristics of the noise accurately so the performance might deteriorate. To overcome that problem, we propose time propagation convolution algorithms adopting Moment Matched Gaussian Kernel (MMGK) on regular grids through mass linear interpolation. To extend the dimension of the MMGK that can accurately describe the noise moments up to the kernel length, we propose the extended MMGK based on the outer tensor product. The proposed time propagation algorithms using one common kernel through the mass linear interpolation not only improve the performance of the filter but also significantly reduce the computational load. The performance improvement and the computational load reduction of the proposed algorithms are verified through numerical simulations for various nonlinear models.

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“…Thus, it is necessary to investigate switched nonlinear systems. On the other hand, the T‐S fuzzy model has been widely used to describe the complex nonlinear systems 9‐12 . However, the traditional type‐1 fuzzy set does not contain the uncertain information that it cannot model the uncertain nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Memory‐event‐trigger‐based output tracking control for networked switched interval type‐2 fuzzy systems against deception attacks

Gao

Shi

Zhou

et al. 2023

Intl J Robust & Nonlinear

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This paper investigates the problem of event-triggered output tracking control for networked switched interval type-2 (IT2) fuzzy systems under deception attacks. Different from the existing event-triggered method, a new type of event-triggered scheme which called the memory event-triggered scheme (METS) is proposed to save the network resources, under which some past released triggering events are used to generate the new events. Also, the network communication may be corrupted by deception attacks and the tracking control is meaningful. We give some conditions to guarantee the H ∞ output tracking performance of the closed loop memory event-triggered networked switched IT2 fuzzy systems under deception attacks in secure sense. Finally, a simulation example validates the proposed technique.

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Fuzzy-Affine-Model-Based Sampled-Data Filtering Design for Stochastic Nonlinear Systems

Cited by 34 publications

References 39 publications

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Adaptive multi-dimensional Taylor network control for nonlinear stochastic systems with time-delay

Reliable Time Propagation Algorithms for PMF and RBPMF

Memory‐event‐trigger‐based output tracking control for networked switched interval type‐2 fuzzy systems against deception attacks

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