Quantized energy-to-peak state estimation for persistent dwell-time switched neural networks with packet dropouts

Shen, Hao; Huang, Zhengguo; Yang, Xiaofei; Wang, Zhen

doi:10.1007/s11071-018-4322-y

Cited by 20 publications

(10 citation statements)

References 48 publications

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“…In the past decade or so, the energy-to-peak state estimation problem has aroused a great deal of research interest in networked systems subject to several network-induced effects including packet dropouts [46], quantization measurements [34], protocol scheduling effects [36] and event-triggered transmis-sions [38]. For example, in [36], a model-dependent estimator has been developed to deal with the energy-to-peak state estimation problem under high-rate communication channel with Round-Robin protocol scheduling effects.…”

Section: Introductionmentioning

confidence: 99%

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Zou

Wang

Dong

et al. 2022

IEEE Trans. Cybern.

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This paper is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e. minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.

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Section: Introductionmentioning

confidence: 99%

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Zou

Wang

Dong

et al. 2022

IEEE Trans. Cybern.

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“…However, due to the limited capability of communication channel, the signal information (e.g., system state, measurement output, and control signal) is usually required to be quantized before it is transmitted into the network. Signal quantization has been widely adopted and studied in recent literatures [35][36][37][38][39][40]. Nevertheless, accompanied with the signal quantization, quantization errors, which may yield system instability and performance degradation, and also bring difficulties for the system analysis and control, inevitably occur.…”

Section: Introductionmentioning

confidence: 99%

Quantized State Feedback-Based H∞ Control for Nonlinear Parabolic PDE Systems via Finite-Time Interval

Chang¹,

Li²,

Park³

2021

Preprint

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In this paper, the finite-time H∞ control problem of nonlinear parabolic partial differential equation (PDE) systems with parametric uncertainties is studied. Firstly, based on the definition of the quantiser, static state feedback controller and dynamic state feedback controller with quantization are presented, respectively. The finite-time H∞ control design strategies are subsequently proposed to analyze the nonlinear parabolic PDE systems with respect to the effect of quantization. And by constructing appropriate Lyapunov functionals for the studied systems, sufficient conditions for the existence of the feedback control gains and the quantizer’s adjusting parameters which guarantee the prescribed attenuation level of H∞ performance are expressed as nonlinear matrix inequalities. Then, by using some inequalities and decomposition technic, the nonlinear matrix inequalities are transformed to standard linear matrix inequalities (LMIs). Moreover, the optimal H∞ control performances are pursued by solving optimization problems subject to the LMIs. Finally, to illustrate the feasibility and effectiveness of the finite-time H∞ control design strategies, an application to the catalytic rod in a reactor is explored.

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“…For example, it could be applied to manufacturing systems, network control systems, fault‐tolerant control systems, and so on. During the past few years, many important results have emerged such as stability, 3‐8 stabilization, 9‐13 state estimation, 14,15 slide mode control, 16‐19 H ∞ and H 2 control 20,21 . Although the above Markov process has ability to describe many practical applications, its sojourn time is exponentially distributed and has nothing to do with transition probability.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of semi‐Markovian jump systems by a stochastically scheduled controller

Wang

Fan

2020

Intl J Robust & Nonlinear

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This article considers the stabilization problem of continuous‐time semi‐Markovian jump systems. A kind of stochastically scheduled controller is proposed to realize the aim. It could also suffer a case that no controller is added to subsystems during some time slices. Based on a Lyapunov approach, sufficient conditions for such a controller are presented with LMI forms. Both sojourn times of jump signal and distribution properties of stochastic scheduling are involved and have positive roles in getting better performances. Two special cases about mode‐dependent and mode‐independent controllers are directly obtained. A practical example is given to verify the effectiveness and superiority of the proposed methods.

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Quantized energy-to-peak state estimation for persistent dwell-time switched neural networks with packet dropouts

Cited by 20 publications

References 48 publications

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Energy-to-Peak State Estimation With Intermittent Measurement Outliers: The Single-Output Case

Quantized State Feedback-Based H∞ Control for Nonlinear Parabolic PDE Systems via Finite-Time Interval

Stabilization of semi‐Markovian jump systems by a stochastically scheduled controller

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