Non-fragile dissipative state estimation for semi-Markov jump inertial neural networks with reaction-diffusion

Sun, Lin; Su, Liyun; Wang, Jing

doi:10.1016/j.amc.2021.126404

Cited by 25 publications

(15 citation statements)

References 43 publications

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“…in which o (𝛥) is the little-o notation with lim 𝛥→0 (o (𝛥) ∕𝛥) = 0; 𝜋 mm (𝛼) = − ∑ 𝜍 n=1,n≠m 𝜋 mn (𝛼) ≥ 0 with m ≠ n describes the TPs from mode m at time t to mode n at time t + 𝛥. Definition 1 (27). If Pr(𝜚 𝜅+1 = n, 𝛼 𝜅+1 ≤ 𝛼|𝜚 0 , … , 𝜚 𝜅 , t 0 , … , t 𝜅 ) = Pr(𝜚 𝜅+1 = n, 𝛼 𝜅+1 ≤ 𝛼|𝜚 𝜅 ) satisfies for each m, n ∈  , t 0 , t 1 , … , t 𝜅 ≥ 0, then the stochastic process {𝜚 (t) , 𝛼} t⩾0 ≜ {𝜚 𝜅 , 𝛼 𝜅 } 𝜅∈N ≥1 taking values in  , is defined as a homogeneous semi-MJ process.…”

Section: 2mentioning

confidence: 99%

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Formation encirclement of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy

Wang

Sun

Ping

et al. 2023

Intl J Robust & Nonlinear

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This paper focuses on the formation encirclement problem of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy. A target encirclement control algorithm is designed to achieve an enclosed formation, which drives autonomous quadrotors around the target so the target can't get away. Afterward, we propose a decentralized consensus‐based control protocol to realize the encirclement of the target quadrotor, where onboard miniature computers carry out all necessary computations. Notably, the quadrotor is modeled by a double integrator and the switching topology is described by a signed graph subjected to the semi‐Markov jump process. Whereafter, some criteria in the form of linear matrix inequalities are constructed based on the Lyapunov stability theory to achieve encirclement behavior. Ultimately, the proposed approach is verified by a comparison simulation and two flight experiments with three follower quadrotors enclosing a leader quadrotor.

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Section: 2mentioning

confidence: 99%

“…

\otimes

represents Kronecker product. Additionally, for other notations, please refer to Reference 27.…”

Section: Introductionmentioning

confidence: 99%

Formation encirclement of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy

Wang

Sun

Ping

et al. 2023

Intl J Robust & Nonlinear

Self Cite

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“…In recent years, much interest has been devoted to developing non-fragile estimators capable of tolerating some perturbations in the estimator gain, and some relevant results have appeared in the literature. [14][15][16][17] For instance, in Reference 14, a simple linear structured nonfragile state estimator was proposed for a class of delayed NNs with nonlinear perturbations. In Reference 15, the problem of nonfragile dissipative state estimation for an inertial NN with reaction diffusion using a semi-Markov jump model was discussed.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

Karnan

Nagamani

2023

Intl J Robust & Nonlinear

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This article aims to design a nonfragile state estimator for neural networks with time-varying delay under the event-triggered mechanism. This paper addresses the two most common issues that exist in large-scale networks: gain variations in the estimator design and lack of network resources. Based on the nonfragile paradigm and an event-triggered mechanism, an estimator is being developed to address this issue, considering both the additive and multiplicative structured gain variations. Through the Lyapunov-Krasovskii functional method and linear matrix inequality technique, sufficient conditions are derived to guarantee the existence of the proposed state estimator by employing the generalized free-weighting matrix inequality and improved reciprocally convex inequality.Moreover, the obtained linear matrix inequalities provide an explicit expression of the estimator gain matrix and triggering matrix. Eventually, two numerical examples are provided to authenticate the proposed findings.

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“…The main idea of this approach is that one could construct a filter and use the output of the filter to estimate the control output of the system. Recently, non-fragile filtering problem attracted great attention due to its can tolerate filter parameters variations without any deration in the system performance (Sun et al, 2021). The non-fragile H ∞ controller was designed in Keyumarsi et al (2021) and Zhang et al (2017) for linear time-invariant systems and uncertain non-linear networked control systems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Non-fragile filtering for delayed semi-Markov jump systems with non-linear perturbation using event-triggered scheme

Xia

et al. 2022

Transactions of the Institute of Measurement and Control

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This paper studies the non-fragile [Formula: see text] filter design problem for a class of delayed semi-Markov jump systems with non-linear perturbation under event-triggered strategy. By applying the Lyapunov stability theory and matrix integral inequalities, sufficient conditions for the stochastic stability and [Formula: see text] performance of the filtering error system are obtained. Then, a co-design method of the filter parameters and event-triggered matrices is developed. Finally, two simulation results are given to illustrate the usefulness of the proposed scheme.

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Non-fragile dissipative state estimation for semi-Markov jump inertial neural networks with reaction-diffusion

Cited by 25 publications

References 43 publications

Formation encirclement of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy

Formation encirclement of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

Non-fragile filtering for delayed semi-Markov jump systems with non-linear perturbation using event-triggered scheme

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