H-Infinity Tracking Controller for Linear Discrete-Time Stochastic Systems with Uncertainties

Nakamori, Seiichi

doi:10.37394/23201.2022.21.26

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…With changes in the operating conditions or environment, the characteristics of the physical object undergo corresponding changes, resulting in errors within the system model, commonly referred to as uncertainty. 34,35 In this article, considering that the system matrix contains uncertainty, it is assumed that…”

Section: Model Descriptionmentioning

confidence: 99%

“…With changes in the operating conditions or environment, the characteristics of the physical object undergo corresponding changes, resulting in errors within the system model, commonly referred to as uncertainty 34,35 . In this article, considering that the system matrix contains uncertainty, it is assumed that

\begin{align} {\overset{𝒜}{true}}_{m_{k} p} = 𝒜_{m_{k} p} + normalΔ 𝒜_{m_{k} p}, \end{align}

where

Δ 𝒜_{m_{k} p} = T_{m_{k} p} Y_{m_{k}} (k) U_{m_{k} p}

represents the variation of system matrix and

{Y}_{m_k}(k)

is bounded by

{Y}_{m_k}^T(k){Y}_{m_k}(k)\le I

.…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Event‐triggered security control for fuzzy‐model‐based cyber‐physical systems under Denial‐of‐Service attacks and actuator faults

Tan,

Gao,

Ding

et al. 2023

Intl J Robust & Nonlinear

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This article addresses the issue of event‐triggered security control for nonlinear cyber‐physical systems, characterized by Denial‐of‐Service (DoS) attacks, time‐varying delays, and actuator faults. These complexities are captured using the Takagi–Sugeno fuzzy model. Significantly, this study focuses on DoS attacks targeting the controller‐to‐actuator channel, and subsequently introduces a model for actuator faults that considers the prolonged utilization or degradation of component parts. To optimize communication resource allocation, a model‐independent event‐triggered mechanism is proposed. Utilizing the Lyapunov–Krasovskii function approach, the objective of achieving stochastic stability is transformed into a linear matrix inequalities problem, thus deriving a set of sufficient conditions. The efficiency and validity of the proposed algorithms is demonstrated through simulation results.

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Section: Model Descriptionmentioning

confidence: 99%

“…With changes in the operating conditions or environment, the characteristics of the physical object undergo corresponding changes, resulting in errors within the system model, commonly referred to as uncertainty 34,35 . In this article, considering that the system matrix contains uncertainty, it is assumed that