$$\mathcal {H}_{\infty }$$ H ∞ filtering for sample data systems with stochastic sampling and Markovian jumping parameters

Revathi, V. M.; Balasubramaniam, P.; Park, Ju H.; Lee, Tae Hee

doi:10.1007/s11071-014-1479-x

Cited by 17 publications

(4 citation statements)

References 41 publications

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“…More precisely, to solve the sampled‐data H ∞ control problem, the sampled‐data system is modeled as a continuous‐time one with an input delay , which the sampling period is assumed to be time varying but bounded delays. In recent years, the H ∞ sampled‐data control problem with system models expressed by dynamical systems has become a popular research topic and has gained extensive attentions . Also, to reduce the conservativeness, some of the researchers have used the free weighting matrix approach for the control design problems and it plays an important role in deriving delay‐dependent stabilization condition .…”

Section: Introductionmentioning

confidence: 99%

Dissipative sampled‐data control of uncertain nonlinear systems with time‐varying delays

et al. 2015

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This article investigates the delay‐dependent robust dissipative sampled‐data control problem for a class of uncertain nonlinear systems with both differentiable and non‐differentiable time‐varying delays. The main purpose of this article is to design a retarded robust control law such that the resulting closed‐loop system is strictly (Q, S, R)‐dissipative. By introducing a suitable Lyapunov–Krasovskii functional and using free weighting matrix approach, some sufficient conditions for the solvability of the addressed problem are derived in terms of linear matrix inequalities. From the obtained dissipative result, we deduce four cases namely, H∞ performance, passivity performance, mixed H∞, and passivity performance and sector bounded performance of the considered system. From the obtained result, it is concluded that based on the passivity performance it is possible to obtain the controller with less control effort, and also the minimum H∞ performance and the maximum allowable delay for achieving stabilization conditions can be obtained via the mixed H∞ and passivity control law. Finally, simulation studies based on aircraft control system are performed to verify the effectiveness of the proposed strategy. © 2015 Wiley Periodicals, Inc. Complexity 21: 142–154, 2016

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Section: Introductionmentioning

confidence: 99%

Dissipative sampled‐data control of uncertain nonlinear systems with time‐varying delays

et al. 2015

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“…Furthermore, based on the relations

- false((σ_{1} - σ_{1} (t)) / σ_{1} (t)) = - 1 + false(1 / σ_{1} (t))

and

- false(σ_{1} (t) / (σ_{1} - σ_{1} (t))) = - 1 + false(1 / ((σ_{1} - σ_{1} (t)))

, it can be considered as one of the reciprocally convex combination in [27, 33], which can be effectively treated as the simple variation of lower bound lemma. Inspired by the work [27, 33], without any conservative approximation, we can find the upper bound of (28) by using (12) and lower bound lemma 3, where

\begin{matrix} right left right left right left right left right left right left0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em 2em 0.278em3pt \\ - {(][, \begin{matrix} \sqrt{\frac{σ_{1} false(t false)}{false(σ_{1} - σ_{1} false(t false) false)}} \int_{t - σ_{1}}^{t - σ_{1} false(t false)} \dot{x} false(s false) d s \\ - \sqrt{\frac{false(σ_{1} - σ_{1} false(t false) false)}{σ_{1} false(t false)}} \int_{t - σ_{1} false(t false)}^{t} \dot{x} false(s false) d s \end{matrix})}^{normalT} [\begin{matrix} center center1em4pt \\ σ<...> \end{matrix}] \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

Exponential stability of impulsive systems with random delays under sampled‐data control

Subramanian

Muthukumar

Zhu

2017

IET Control Theory & Applications

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“…For wireless transmission networks, with the increasing of the amount of data transmitted, packet dropouts and time delays are inevitable in the case of limited communication channel capacity [22]- [24]. How to study networked control systems has become an important problem [25]- [28].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking With Constrained Sensors and Unreliable Communication Networks

Liu

Rao

et al. 2019

IEEE Access

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This work investigates the remote vehicle tracking issue over constrained monitoring sensors and unreliable communication networks. A saturation function is used to describe the bounded time varying acceleration of the vehicle. A set of matrices are introduced to model the sensor monitoring conditions called captured states (CSs), and a Markov chain with time varying and partially unknown transition probability (TVPUTP) is proposed to analyze the conditions of the CSs. Then, a CS dependent nonfragile estimator is designed based on the measured unreliable vehicle information, and the estimation error system (EES) is derived. Two theorems are established to ensure that the EES satisfies the finite-horizon (FH) H ∞ performance. Finally, an example is introduced to show the effectiveness of the results.

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$$\mathcal {H}_{\infty }$$ H ∞ filtering for sample data systems with stochastic sampling and Markovian jumping parameters

Cited by 17 publications

References 41 publications

Dissipative sampled‐data control of uncertain nonlinear systems with time‐varying delays

Dissipative sampled‐data control of uncertain nonlinear systems with time‐varying delays

Exponential stability of impulsive systems with random delays under sampled‐data control

Trajectory Tracking With Constrained Sensors and Unreliable Communication Networks

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